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KAUST-CEMSE-Public-Colloquium-Marco-Canini-Why-ML-Works-Its-Not-Magic

Why ML Works: It’s Not Magic, It’s Networked Systems Wizardry!

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Today’s machine learning (ML) solutions are achieving extraordinary success across diverse fields, thanks to their ability to learn complex models and deliver impressively accurate results. While the availability of big data is often credited as a driving force, the unsung hero behind ML’s rise is the rapid innovation in computing and software systems that make these breakthroughs possible and accessible.

Hayssam Dahrouj

Senior Research Scientist at KAUST (King Abdullah University of Science and Technology)

Karen Sanchez

Postdoctoral Research Fellow
Karen Sanchez is a Postdoctoral Researcher in the IVUL lab at KAUST, specializing in deep learning, machine learning, and artificial intelligence (AI) for healthcare applications. Her research focuses on video understanding, domain adaptation, generative AI, and methods for preserving patient privacy.

Hani A. Al Majed

M.S. Student
Electrical and Computer Engineering Master's student at KAUST under the supervision of Professor Bernard Ghanem, with expertise in machine learning workflow automation, physics-informed neural operators, and AI for Science, particularly in chemistry applications. Passionate about leveraging AI to solve complex scientific problems.

Ahmad Sait

M.S. Student, Computer Science
Ahmad Sait is a Master's student in the Computer Science program at KAUST. Before joining KAUST, Ahmad obtained his Bachelor's degree in Computer Science from King Abdulaziz University, Saudi Arabia. Ahmad is also a KAUST Academy graduate from the AI track, having been under the supervision of Prof. Naeemullah Khan. The Academy provided him with seven opportunities to assist in teaching students enrolled in subsequent cohorts.

Saleh AlJazzar

Research Fellow at King Abdullah University of Science and Technology (KAUST) .

Muhammad Esmat

Visiting student at, King Abdullah University of Science and Technology (KAUST).

Ebrahim Al-Safadi

Intern, Information Science Lab at King Abdullah University of Science and Technology (KAUST),

Yasser Jaber

Visiting Student Researcher ,Machine Learning Engineer at KAUST (King Abdullah University of Science and Technology).
Syed Abidullah Fareed

Syed Abidullah Fareed

Visiting Student (former), Information Science Lab

Ahsan Mehmood

Intern (former), Information Science Lab
Visiting student at King Abdullah University of Science and Technology (KAUST).

Abdulkarem Badiea Alshaibani

Visiting Student at the Information Science Lab at the King Abdullah University of Science and Technology (KAUST).

Advanced Breach and Attack Simulation using ML

Cybersecurity is becoming a need more than ever. Organizations need to protect their digital assets and are required to earn certifications to prove the compliance to the regulations and rules. For this, these assets must be assessed to ensure the target security posture and to get certified or pass an audit on yearly basis. This is a daunting and costly task as it often requires a third-party tester that tries to penetrate the system, under agreement. Breach and Attack Simulation is a new method that allows to do this penetration testing in-house, using some automation tools. Some of these

Novel Data Mining Methods for Virtual Screening of Biological Active Chemical Compounds by Othman Soufan

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H2 B9

Abstract Drug discovery is a process that takes many years and hundreds of millions of dollars to reveal a con dent conclusion about a specific treatment. Part of this sophisticated process is based on preliminary investigations to suggest a set of chemical compounds as candidate drugs for the treatment. Computational resources have been playing a significant role in this part through a step known as virtual screening. From a data mining perspective, the availability of rich data resources is key in training prediction models. Yet, the difficulties imposed by big expansion in data and its

Contributions to In Silico Genome Annotation

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B3 L5 R5209

Abstract Genome annotation is an important topic since it provides information for the foundation of downstream genomic and biological research. It is considered as a way of summarizing part of existing knowledge about the genomic characteristics of an organism. Annotating different regions of a genome sequence is known as structural annotation while identifying functions of these regions are considered as a functional annotation. In silico approaches can facilitate both tasks that otherwise would be difficult and time-consuming. This study contributes to genome annotation by introducing

Mining Genome-Scale Growth Phenotype Data through Constant-Column Biclustering

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B3 L5 R5209

Growth phenotype profiling of genome-wide gene-deletion strains overstresses conditions can offer a clear picture that the essentiality of genes depends on environmental conditions. In this dissertation, we first demonstrate that detecting such "co-fit" gene groups can be cast as a less well-studied problem in biclustering, i.e., constant-column biclustering. Despite significant advances in biclustering techniques, very few were designed for mining in growth phenotype data.

Computational Methods for ChIP-seq Data Analysis and Applications

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B3 L5 5209

Abstract The development of Chromatin immunoprecipitation followed by sequencing (ChIP-seq) technology has enabled the construction of genome-wide maps of protein-DNA interaction. Such maps provide information about transcriptional regulation at the epigenetic level (histone modifications and histone variants) and at the level of transcription factor (TF) activity. This dissertation presents novel computational methods for ChIP-seq data analysis and applications. The work of this dissertation addresses four main challenges. First, I address the problem of detecting histone modifications from

Novel Computational Methods to Predict Drug–target Interactions Using Graph Mining and Machine Learning Approaches

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B3 L5 R5220

Abstract Computational drug repurposing aims at finding new medical uses for existing drugs. The identification of novel drug-target interactions (DTIs) can be a useful part of such a task. Finding computationally DTIs is a convenient strategy to identify potentially new DTIs at low cost with reasonable accuracy. However, the current DTI prediction methods suffer a high false positive prediction rate. Here, we present a comprehensive review of the recent progress in the field of DTI prediction from data-centric and algorithmic-centric perspectives that can help in constructing novel reliable

Genetic Algorithms for Optimization of Machine-learning Models and their Applications in Bioinformatics

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B3 L5 R5209

Abstract ​​​Machine-learning (ML) techniques have been widely applied to solve different problems in biology. However, biological data are large and complex, which often results in extremely intricate ML models. Frequently, these models may have poor performance or may be computationally unfeasible. This study presents a set of novel computational methods and focuses on the application of genetic algorithms (GAs) for the simplification and optimization of ML models and their applications to biological problems. The dissertation addresses the following three challenges. The first challenge is

Novel Computational Methods that Facilitate Development of Cyanofactories for Free Fatty Acid Production by Olaa Motwalli

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B3 L5 R5209

Abstract Finding a source from which high-energy-density biofuels can be derived at an industrial scale has become an urgent challenge for renewable energy production. Some microorganisms can produce free fatty acids (FFA) as precursors towards such high-energy-density biofuels. In particular, photosynthetic cyanobacteria are capable of directly converting carbon dioxide into FFA. However, current engineered strains need several rounds of engineering to reach the level of FFA production for it to be commercially viable. Thus, new chassis strains that require less engineering are needed

KAUST Research Workshop on Optimization and Big Data

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B19 L3 H2

The age of "big data" is here: data of unprecedented sizes is becoming ubiquitous, which brings new challenges and new opportunities. With this comes the need to solve optimization problems of unprecedented sizes.

Neural Inductive Matrix Factorization for Predicting Disease-Gene Associations

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B3 R5208

In silico prioritization of undiscovered associations can help find causal genes of newly discovered diseases. Some existing methods are based on known associations and side information of diseases and genes. We exploit the possibility of using a neural network model, Neural Inductive Matrix Completion (NIMC) in disease-gene prediction.

AI4GH Seminar Series - Machine Learning with Biological Knowledge Graphs

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B2 R5220

Abstract Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval, and federated queries. In the past years, feature learning methods that are applicable to graph-structured data are becoming available, but have not yet widely been applied and evaluated on structured biological knowledge. We develop novel methods for feature learning on biological knowledge graphs and apply them to the prediction of edges in the knowledge graph representing problems of finding candidate genes of diseases, or drugs

AI4GH Seminar Series - Variant Prioritization in Cancer: Understanding and Prediction of Cancer Driver Genes and Mutations

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B2 R5220

Sequencing has identified millions of somatic mutations in human cancers. Identifying and distinguishing cancer driver genes amongst the millions of candidate mutations remains a major challenge.

Contributions to Computational Methods for Association Extraction from Biomedical Data: Applications to Text Mining and In Silico Toxicology

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B2 L5 R5220

Abstract The task of association extraction involves identifying links between different entities. Here, we make contributions to two applications related to the biomedical field. The first application is in the domain of text mining aiming at extracting associations between methylated genes and diseases from biomedical literature. Gathering such associations can benefit disease diagnosis and treatment decisions. We developed the DDMGD database to provide a comprehensive repository of information related to genes methylated in diseases, gene expression, and disease progression. Using DEMGD, a

AI4GH Seminar Series - Vector Representation of Biological Entities Based on Ontologies and Their Annotations

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B2 R5220

Biological knowledge is widely represented in the form of ontology-based annotations: ontologies describe the phenomena assumed to exist within a domain, and the annotations associate a biological entity with a set of phenomena within the domain.

Towards rational design of biosynthesis pathways

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B3 R5209

Recent advances in genome editing and metabolic engineering enabled a precise construction of de novo biosynthesis pathways for high-value natural products. One important design decision to make for the engineering of heterologous biosynthesis systems is concerned with which foreign metabolic genes to introduce into a given host organism.

Prediction of active and inactive chemical compounds from high-throughput assays

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B5 L5 R5209

Abstract This study considers chemical compounds that can exert their activity by interacting with a target protein or other molecular receptor. Our aim is to develop machine learning models that can predict if a chemical compound will be active in a particular test/assay. We will use data from assays that are present in the PubChem knowledge base, specifically in its segment called BioAssays which reports the results of many high-throughput screening experiments. PubChem BioAssays is a valuable resource that contains information from a large number of experiments. In one assay, sometimes many

AI4GH Seminar Series - Towards Rational Design of Biosynthesis Pathways

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B2 R5220

Recent advances in genome editing and metabolic engineering enabled a precise construction of de novo biosynthesis pathways for high-value natural products. One important design decision to make for the engineering of heterologous biosynthesis systems is concerned with which foreign metabolic genes to introduce into a given host organism.

Variant Prioritization in Cancer: Understanding and Prediction of Cancer Driver Genes and Mutations

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B3 R5209

Abstract Sequencing has identified millions of somatic mutations in human cancers. Identifying and distinguishing cancer driver genes amongst the millions of candidate mutations remains a major challenge. Accurate identification of driver genes and driver mutations is critical for advancing cancer research and personalizing treatment based on accurate stratification of patients. Due to inter-tumor genetic heterogeneity, many driver mutations within a gene occur at low frequencies, which make it challenging to distinguish them from other non-driver mutations. Motivated by these challenges, we

VSIM: Visualization and Simulation of Genomes for Premarital Testing

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B3 L5 R5209

Abstract Interpretation and simulation of the large-scale genomics data are very challenging, and currently, many web tools have been developed to analyze genomic variation which supports automated visualization of a variety of high throughput genomics data. We have developed VSIM an automated and easy to use web application for interpretation and visualization of a variety of genomics data, it identifies the candidate disease variants by referencing to four databases Clinvar, GWAS, DIDA, and PharmGKB, and predicted the pathogenic variants. Moreover, it investigates the attitude towards

AI4GH Seminar Series - Genome-scale Regression Analysis Reveals a Linear Relationship for Promoters and Enhancers After Combinatorial Drug Treatment

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B2 R5220

Drug combination therapy for the treatment of cancers and other multifactorial diseases has the potential of increasing the therapeutic effect while reducing the likelihood of drug resistance. In order to reduce the time and cost spent on comprehensive screens, methods are needed which can model additive effects of possible drug combinations.

AI4GH Seminar Series - VSIM: Visualization and Simulation of Genomes for Premarital Testing

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B2 R5220

Interpretation and simulation of the large-scale genomics data are very challenging, and currently, many web tools have been developed to analyze genomic variation which supports automated visualization of a variety of high throughput genomics data.

The Evolution of Machine Learning

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B4 B5 A0215

This talk presents an overview of recent methodological advances in developing item-based nearest-neighbor-based recommender systems that have substantially improved their performance. Biography George Karypis is a Distinguished McKnight University Professor and an ADC Chair of Digital Technology at the Department of Computer Science & Engineering at the University of Minnesota, Twin Cities. His research interests span the areas of data mining, high performance computing, information retrieval, collaborative filtering, bioinformatics, cheminformatics, and scientific computing. His research has

Semantic Prioritization of Novel Causative Genomic Variants in Mendelian and Oligogenic Diseases

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B9 R4223

Abstract Recent advances in Next Generation Sequencing (NGS) technologies have facilitated the generation of massive amounts of genomic data which in turn is bringing the promise that personalized medicine will soon become widely available. As a result, there is an increasing pressure to develop computational tools to analyze and interpret genomic data. In this dissertation, we present a systematic approach for interrogating patients' genomes to identify candidate causal genomic variants of Mendelian and oligogenic diseases. To achieve that, we leverage the use of biomedical data available

Deep Predictive Models for Mining Electronic Health Records

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B1 L4 R4214

There is an increasing growth in the amount of electronic health records (EHRs) being collected by healthcare facilities. Data mining techniques hold great potential to systematically use such data for identifying not only inefficiencies but also best practices that improve care and reduce costs. However, due to the complexity of EHR data, directly applying traditional machine learning techniques may yield unsatisfactory predictive performance. Recent advances in deep learning-based methods provide unprecedented ability to predict patients’ future health status, but they still suffer from the sparsity issue of EHR data.

Convolutional Neural Networks on Graphs

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B9 H2

In the past years, deep learning methods have achieved unprecedented performance on a broad range of problems in various fields from computer vision to speech recognition. So far research has mainly focused on developing deep learning methods for grid-structured data, while many important applications have to deal with graph-structured data.

Day 2 - Deep learning and the latest algorithms in artificial intelligence

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B4 B5 A0215

The ML Hub offers a 2-day short course on deep learning and the latest algorithms in artificial intelligence. The course will be given by Professor Xavier Bresson from the Nanyang Technological University (NTU) in Singapore, who is a leading researcher in the field of deep learning. The course will include the theory of deep learning techniques as well as practical exercises. Prerequisite knowledge: Basic knowledge of linear algebra (e.g. matrix multiplication) and script programming (e.g. Python, Matlab, R) are needed. The coding will be done in Python. Note, that this course has limited seating and filling registration form does not guarantee acceptance. If you are selected, you will receive a confirmation e-mail.

From Bioinformatics to Translational and Health Informatics: Whitening the Black Box

Prof. Yasser EL-Manzalawy, Information Sciences and Technology, Pennsylvania State University

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B9 H1

Machine learning has been extensively used in developing predictive models for a variety of bioinformatics tools. In many of these applications, to achieve the highest possible predictive performance, black box models (e.g., SVMs and Neural Networks) had been preferred over white box models (e.g., Decision Trees and Rule-based models). First, I argue that sacrificing interpretability for the sake of performance is a reasonable decision for many bioinformatics applications. However, I will demonstrate that uncareful utilization of black box models can lead to misinterpretations of the results.

Day 1 - Deep learning and the latest algorithms in artificial intelligence

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B4 B5 A0215

The ML Hub offers a 2-day short course on deep learning and the latest algorithms in artificial intelligence. The course will be given by Professor Xavier Bresson from the Nanyang Technological University (NTU) in Singapore, who is a leading researcher in the field of deep learning. The course will include the theory of deep learning techniques as well as practical exercises. Prerequisite knowledge: Basic knowledge of linear algebra (e.g. matrix multiplication) and script programming (e.g. Python, Matlab, R) are needed. The coding will be done in Python. Note, that this course has limited seating and filling registration form does not guarantee acceptance. If you are selected, you will receive a confirmation e-mail.

Modern Techniques of Statistical Optimization for Machine Learning

Tong Zhang, Professor of Computer Science and Mathematics, HKUST

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B9 H1

Many problems in machine learning rely on statistics and optimization. To solve these problems, new techniques are needed. I will show some of these new techniques through selected machine learning problems I have recently worked on, such as nonconvex stochastic optimization, distributed training, adversarial attack, and generative models.

Zero in on computational challenges in Nanopore sequencing

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B9 L2 H1

In this talk, I will first give an overview of the research activities in Structural and Functional Bioinformatics Group (http://sfb.kaust.edu.sa). I will then focus on our efforts on developing computational methods to tackle key open problems in Nanopore sequencing. In particular, I will introduce our recent works on developing a collection of computational methods to decode raw electrical current signal sequences into DNA sequences, to simulate raw signals of Nanopore, and to efficiently and accurately align electrical current signal sequences with DNA sequences. Then, I will further introduce their applications in clinical and environmental fields.

Reproducibility in Computer Science - Part 1

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Langevin Monte Carlo as an optimization algorithm

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B9 L2 H1 R2322

Adil Salim is mainly interested in stochastic approximation, optimization, and machine learning. He is currently a Postdoctoral Research Fellow working with Professor Peter Richtarik at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST).

Reproducibility in Computer Science - Part 2

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Machine Learning Models for Context Aware Recommendation Systems

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B3 L5 R5209

The goal of this thesis is to pave the way towards the next generation of recommendation systems tackling such real-world challenges to improve the user experience while giving good recommendations.

Reproducibility in Computer Science - Part 3

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Practical Cryptographic Framework to Manage Security and Privacy for IoT Endpoint devices

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B1 L4 R4214

In this talk, the speaker will provide a high-level introduction to his recent research on IoT endpoint security. Firstly, he will introduce requirements followed by a discussion on cryptographic algorithm implementation. He will mainly focus on an overview of efficient cryptography for IoT endpoints and system privacy issues. Then he will discuss security management approaches, positives, negatives and challenges to resolve, linking to the endpoint device security section with regards to realistic device needs/capabilities.

Seeing is Believing? Media Integrity in a Post-Truth World

Prof. Nasir Memon, Vice Dean for Academics and Student Affairs and Professor of Computer Science and Engineering at the New York University Tandon School of Engineering

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B9 L2 H1

The emergence of “fake news” along with sophisticated techniques using machine learning to create realistic looking media such as deepfakes, has led to a renewed interest in digital media forensics. In this talk, Professor Nasir Memon will broadly discuss how media is generated and manipulations have been traditionally detected. He will then look at new approaches using machine learning for creating media that are leading us to a world where images and video cannot be believed any more as they can evade traditional detection techniques. Professor Memon will end by discussing approaches that are being developed to return integrity and trust in digital media.

Amazon Web Services (AWS) ML Immersion Day

Mohib Khan, Hesham Abouelmagd, Shijaz Abdulla (AWS)

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B19 H1

The ML Hub with the support of the AI Initiative, is excited to be hosting the AWS ML Immersion Day! Join us for a full-day immersion tutorial and hands-on lab on Amazon’s ML tools. The program includes an introduction to AWS AI and machine learning services and hands-on module on Amazon Lex and SageMaker.

Machine Learning Scan of Complete Genome Sequences of 4,500 Rice Varieties reveals Secrets in the Domestication Process of Crops

Takashi Gojobori, Distinguished Professor, Bioscience

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B9 L2 H2 R2325

In the history of humankind, domestication was invented as anthropogenic evolution that fulfills mankind’s critical food demand. The domestication is simply based on mating processes and subsequent selection processes to pick up better hybrid offspring that have advantageous combinations of genomes. Taking advantage of machine learning classifier, we discovered a number of sub-genomic regions that have been incorporated in the rice genomes through production of hybrid offspring during domestication. This so-called “introgression” event is disclosed as an essential key of domestication process. This eventually leads to construction of the AI-aided Smart Breeding Platform to accumulate all the breeding histories of crop species into an Integrated Breeding Knowledgebase.

Novel computational methods for promoter identification and analysis

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B2 L5 R5209

In this dissertation, I present the methods I have developed for prediction of promoters for different organisms. Instead of focusing on the classification accuracy of the discrimination between promoter and non-promoter sequences, I predict the exact positions of the TSS inside the genomic sequences, testing every possible location. The developed methods significantly outperform the previous promoter prediction programs by considerably reducing the number of false positive predictions. Specifically, to reduce the false positive rate, the models are adaptively and iteratively trained by changing the distribution of samples in the training set based on the false positive errors made in the previous iteration. The new methods are used to gain insights into the design principles of the core promoters. Using model analysis, I have identified the most important core promoter elements and their effect on the promoter activity. I have developed a novel general approach to detect long range interactions in the input of a deep learning model, which was used to find related positions inside the promoter region. The final model was applied to the genomes of different species without a significant drop in the performance, demonstrating a high generality of the developed method.

Stylistic and spatial disentanglement in GANs

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KAUST

This dissertation tackles the problem of entanglement in Generative Adversarial Networks (GANs). The key insight is that disentanglement in GANs can be improved by differentiating between the content, and the operations performed on that content. For example, the identity of a generated face can be thought of as the content, while the lighting conditions can be thought of as the operations.

Rising Stars in AI Symposium 2022 at KAUST

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B4 B5 A0215

Rising Stars in AI Symposium 2022 at KAUST The "Rising Stars in AI Symposium" is geared towards young researchers (including PhD students, PostDocs, young faculty) who have recently published promising work at leading venues. There will be dozens of brief in-person presentations about papers recently accepted at major AI conferences such as NeurIPS, CVPR, EMNLP, ACL, ICML, ICLR, etc. All speakers will be encouraged to start with an intro for non-AI experts. To view the complete program and for more event details, please visit the symposium website. Attend the Symposium The symposium will be

Omics-based Metastasis Prediction using Machine Learning and Deep Learning

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KAUST

Knowing metastasis is the primary cause of cancer-related deaths incentivized research to unravel the complex cellular processes that drive the metastasis. Advancement in technology and specifically the advent of high-throughput sequencing provides knowledge of such processes. This knowledge led to the development of therapeutic and clinical applications. In this regard, predicting metastasis onset has also been explored using artificial intelligence (AI) approaches that are machine learning (ML), and more recently, deep learning (DL).

To Encourage or to Restrict: the Label Dependency in Multi-Label Learning

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KAUST

Multi-label learning addresses the problem that one instance can be associated with multiple labels simultaneously. More or less, these labels are usually dependent on each other in different ways. Understanding and exploiting the Label Dependency (LD) is well-accepted as the key to build high-performance multi-label classifiers, i.e., classifiers having abilities including but not limited to generalizing well on clean data and being robust under evasion attack.

Drug Repositioning through the Development of Diverse Computational Methods using Machine Learning, Deep Learning, and Graph Mining

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KAUST

In this dissertation, we combined artificial intelligence and machine/deep learning with chemical and biological properties to develop several computational methods to solve biomedical domain problems, specifically drug repositioning, and demonstrated their efficiencies and capabilities. We developed three network-based DTI prediction methods using machine learning, graph embedding, and graph mining. These methods significantly improved prediction performance, and the best-performing method even reduces the error rate by more than 33% across all datasets compared to the best state-of-the-art method. As it is more insightful to predict continuous values that indicate how tightly the drug binds to a specific target, we conducted a comparison study of current regression-based methods that predict drug-target binding affinities (DTBA). Our methods demonstrated their efficiency and capability by achieving high prediction performance and identifying therapeutic targets for several cancer types. We further conducted a lung cancer case study of findings that support the novel predicted targets.

Communication-Efficient Distributed Newton-type Algorithms for Federated Learning

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B9 L2 R2322 H1

In this talk, I will discuss communication compression and aggregation mechanisms for curvature information in order to reduce these costs while preserving theoretically superior local convergence guarantees.

Designing High-Performance and Scalable Middleware for HPC, AI, and Data Science

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B9 L2 R2322 H1

This talk will focus on challenges and opportunities in designing middleware for HPC, AI (Deep/Machine Learning), and Data Science. We will start with the challenges in designing runtime environments for MPI+X programming models by considering support for multi-core systems, high-performance networks (InfiniBand and RoCE), GPUs, and emerging BlueField-2 DPUs. Features and sample performance numbers of using the MVAPICH2 libraries will be presented. For the Deep/Machine Learning domain, we will focus on MPI-driven solutions to extract performance and scalability for popular Deep Learning frameworks (TensorFlow and PyTorch), large out-of-core models, and Bluefield-2 DPUs.

Optimal Algorithms for Affinely Constrained, Distributed, Decentralized, Minimax, and High-Order Optimization Problems

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B5 R5220

In this thesis, we discuss a few fundamental and well-studied optimization problem classes: decentralized distributed optimization (Chapters 2 to 4), distributed optimization under similarity (Chapter 5), affinely constrained optimization (Chapter 6), minimax optimization (Chapter 7), and high-order optimization (Chapter 8). For each problem class, we develop the first provably optimal algorithm: the complexity of such an algorithm cannot be improved for the problem class given. The proposed algorithms show state-of-the-art performance in practical applications, which makes them highly attractive for potential generalizations and extensions in the future.

A new class of random field models for data on networks

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B9 L2 R2322 H1

Random fields are popular models in statistics and machine learning for spatially dependent data on Euclidian domains. However, in many applications, data is observed on non-Euclidian domains such as street networks. In this case, it is much more difficult to construct valid random field models. In this talk, we discuss some recent approaches to modeling data in this setting, and in particular define a new class of Gaussian processes on compact metric graphs.

Applied AI for sustainable development

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B9 L2 R2322 H1

Skoltech Applied AI center’s mission is to create AI models and frameworks for solving the problems of sustainable development of industry and economy. In my presentation, I will overview the current center's activities, applied and fundamental problem statements, and corresponding recent results.

Parameter-Free Online Optimization: Past, Present, and Future

Francesco Orabona, Associate Professor of Electrical and Computer Engineering, Boston University

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B9 L2 R2322

Parameter-free online optimization is a class of algorithms that does not require tuning hyperparameters, yet they achieve the theoretical optimal performance. Moreover, they often achieve state-of-the-art performance too. An example would be gradient descent algorithms completely without learning rates. In this talk, I review my past and present contributions to this field. Building upon a fundamental idea connecting optimization, gambling, and information theory, I discuss selected applications of parameter-free algorithms to machine learning and statistics. Finally, we conclude with an overview of the future directions of this field.

Towards Richer Video Representation for Action Understanding

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B2 L5 R5209

With video data dominating the internet traffic, it is crucial to develop automated models that can analyze and understand what humans do in videos. Such models must solve tasks such as action classification, temporal activity localization, spatiotemporal action detection, and video captioning. This dissertation aims to identify the challenges hindering the progress in human action understanding and propose novel solutions to overcome these challenges.

Machine-learning-assisted Quality of Transmission Estimation in Optical Networks

Andrea Bianco, Full Professor, Electronics and Telecommunications Department at Politecnico di Torino

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B9 L4 R4125

Machine Learning (ML) tools have recently been adopted for a wide range of automated operations in optical networking, moving fundamental steps towards the paradigm of zero-touch infrastructures. One example of such tasks is estimating the Quality of transmission of a lightpath prior to its establishment, which is particularly challenging due to the non-linear characteristics of signal propagation in optical fibers and to the often-incomplete knowledge of equipment parameters. This talk provides an overview of the contribution of my research team in the field of ML-based lightpath QoT estimation, including transfer learning approaches for inter-domain model adaptation, active learning for model building with small-sized training dataset, quantification of prediction uncertainty, and adoption of Explainable AI framework to expose the internal decisional mechanisms of trained models.

Exploiting Data Sparsity in Matrix Algorithms for Adaptive Optics and Seismic Imaging

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B2 R5209;

This thesis addresses the exponential growth of experimental data and the resulting computational complexity seen in two major scientific applications, which account for most cycles consumed on today's supercomputers.

Physics-based Simulation in Visual Computing

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B9 L2 H2 H2

This talk covers a selection of previous and current work presenting a broad spectrum of research highlights ranging from simulating stiff phenomena such as the dynamics of fibers and textiles, over liquids containing magnetic particles, to the development of complex ecosystems and weather phenomena. Moreover, connection points to the growing field of machine learning are addressed and an outlook is provided with respect to selected technology transfer activities.

Mitigating Bias in Predictions from Machine Learning Models

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B9 L2 H2 H2

The increasing popularity of machine learning models in real-world automated and decision support systems has underscored the need for assessing and then mitigating biases that may manifest, often spuriously, in their predictions either at the population, sub-population, or individual level. These biases can be assessed in terms of calibration, performance stratification, fairness metrics, prediction interval coverages, etc., and are mainly due to poor model specification (e.g., overparameterization without regularization or loss/likelihood mismatch) or data collection issues (e.g., population misrepresentation or unmeasured confounders).

Security and privacy in centralized and decentralized machine learning

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B4 L5 R5209

Machine learning (ML) is vulnerable to security and privacy attacks. Whereas security attacks aim at preventing model convergence or forcing convergence to wrong models, privacy attacks attempt to disclose the data used to train the model.

FADO: A Federated Learning Attack and Defense Orchestrator

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B5 L5 R5209

Federated Learning (FL) is a distributed machine learning approach that allows multiple parties to train a model collaboratively without sharing sensitive data.

Efficient Machine Learning across Hardware, Software, and Algorithms

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Deep neural networks (DNNs) are revolutionizing computing, necessitating an integrated approach across the computing stack to optimize efficiency. In this talk, I will explore the frontier of DNN optimization, spanning algorithms, software, and hardware. We'll start with hardware-aware neural architecture search, demonstrating how tailoring DNN architectures to specific hardware can drastically enhance performance.

Filling in the blanks of virtual cities

1 min read ·
New software generates three-dimensional models of buildings, with applications for disaster relief support.

CEMSE Big Data Open Day shows off fascinating discoveries

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The fifth-anniversary event marked CEMSE's biggest successes and featured 20 KAUST discoveries, with more than 1,000 visitors from the community attending the event."Big data has many applications: to understand medicine better; to manage food supplies, and to connect objects. Data is at the center of everything," said Dean Mootaz Elnozahy of the University's Computer, Electrical, Mathematical Science and Engineering (CEMSE) Division at the CEMSE Big Data Open Day held on December 4, 2016.

Professors discuss big data and human health in latest Sci-Café

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​Professors Takashi Gojobori, Xin Gao and Robert Hoehndorf spoke about big data and human health at the Sci-Café event held on February 27. Their talk, which was moderated by James Calvin, Vice President for Academic Affairs, aimed to answer the question, "Can Big Data Solve My Health Problems?" This event was open to all members of the KAUST community.

Building SANDS at KAUST

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​​​​​We live in a connected world where networked systems play an increasingly important role. These systems, which are the foundational pillars of our modern digital lives, are the result of some remarkable technological advancements and progress in computer science over the past three decades.

Student life: studying and working at the Computational Bioscience Research Center (KAUST)

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For Ph.D. students Arturo Magana Mora, Haitham Ashoor and Olaa Motwalli, April 2017 was a particularly nerve-wracking month. After years of hard work and sleepless nights, they finally defended their thesis, bringing them one step closer to graduation.

Cocoon finally out: Professor Marco Canini together with VMware, Samsung, and Microsoft craft the ultimate SDN ally

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With the recent emergence of software-defined networking, which brings programmability and lowers the barrier for new functionalities into networks, the academic and industry communities have become very interested in the problem of network verification.

Facebook AI new language explained by KAUST Professor Xiangliang Zhang

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That is not a Messenger troll, but the actual conversation between two chatbots by the Facebook AI Research (FAIR). The two robots, Bob and Alice, were thought the art of negotiation apples and books. They had been instructed to work out how to negotiate between themselves and improve their bartering as they went along. But, after leaving the pair alone, they start talking in this uncompressible but yet effective vocabulary.

Dominik L. Michels receives the first Procter & Gamble Faculty Award

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Dominik L. Michels, Computer Science and Mathematics faculty in the Computer, Electrical and Mathematical Science and Engineering Division (CEMSE) and head of the Computational Sciences Group within the Visual Computing Center (VCC), recently received the first Procter & Gamble (P&G) Faculty Award.

Alumni Feature: Arturo Magana Mora

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CBRC Ph.D. graduate Arturo Magana Mora is currently a postdoctoral researcher at the prestigious National Institute of Advanced Industrial Science and Technology (AIST) in Tokyo, Japan.

Siqing Hou defends his Master’s thesis

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Siqing Hou, a Master's student under the supervision of Associate Professor Xin Gao, successfully defended his thesis.

Alumni Feature: Abdullah Khamis

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Abdullah Khamis has had quite an impressive career so far. After graduating with a Ph.D. degree in Computer Science from KAUST under the supervision of Professor Vladimir Bajic and Associate Professor Xin Gao, he now works as the Director of Data Science in the Center for Strategic Studies (CSS) of the Royal Court of Saudi Arabia.

Meshari Alazami defends his thesis proposal

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Meshari Alazami, a Ph.D. student under the supervision of Associate Professor Xin Gao, successfully defended his thesis proposal on May 22, 2018. His topic, "Towards Rational Design of Biosynthesis Pathways", was well received by committee members Associate Professor Xin Gao, Distinguished Professor Takashi Gojobori and Assistant Professor Robert Hoehndorf.

Maxat Kulmanov defends his thesis proposal

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Maxat Kulmanov, a Ph.D. candidate under the supervision of Assistant Professor Robert Hoehndorf, defended his thesis proposal on July 31, 2018.

New Book "Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining" of Professor Moshkov and His Former Students is Published by Springer

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A new book - "Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining" by Hassan AbouEisha, Talha Amin, Igor Chikalov, Shahid Hussain, and Mikhail Moshkov - was recently published by Springer.

Alumni Focus: Ahmed Abdulmajeed Alabdulkarim, M.S. '11

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Ahmed Abdulmajeed Alabdulkarim earned a master's degree from KAUST in 2011. His research interests are in the field of big data, artificial intelligence and machine learning, and he is now pursuing his Ph.D. at the Massachusetts Institute of Technology (MIT) in the U.S. He received his bachelor's degree with honors in computer science from King Saud University, where he graduated at the top of his class. From a young age, Alabdulkarim was passionate about computers. Studying computer science was his drive and motivation to continue his university studies. He explained that he chose KAUST for

Mona Alshahrani defends her thesis proposal

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Mona Alshahrani, a Ph.D. candidate under the supervision of Assistant Professor Robert Hoehndorf, defended her thesis proposal on October 10, 2018.

Computing solutions for biological problems

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Xin Gao’s research is building bridges between computer science and biology.

Meshari Alazmi defends his Ph.D. thesis

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Meshari Alazmi, a Ph.D. candidate under the supervision of Associate Professor Xin Gao, defended his Ph.D. dissertation on October 24, 2018.

Sara Althubaiti defends her Master's thesis

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Sara Althubaiti, a student under the supervision of Assistant Professor Robert Hoehndorf, defended her Master's thesis on November 5, 2018.

Tweaking tools to track tweets over time

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Computer model learns to identify Twitter users’ evolving interests by analyzing their Tweets.

Accelerating the grapevine effect

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By looking at classical gossip algorithms from a novel perspective, KAUST Professor Peter Richtarik has found a way to significantly speed up gossip-based information sharing, and in the process, he discovered new applications for this efficient mathematical approach. Gossip involves the sharing of information between individuals in a network and can be applied mathematically in both human social networks and data networks, such as distributed sensors. “A network is a collection of nodes, each connected to other nodes via links,” explains Richtarik. “In social networks, for instance

KAUST Researchers Are the First to Generate Images of Realistic and Highly Detailed Texture Maps of Gigapixel in Size Using Deep Neural Networks

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KAUST researchers Anna Fruehstueck, Dr. Ibraheem Alhashim, and Prof. Peter Wonka have developed a novel technique to generate images of realistic and highly detailed texture maps using deep neural networks. The texture images synthesized by their system TileGAN can be of gigapixel size and are created by seamlessly merging smaller texture blocks into a single large image. The underlying neural networks are trained using high-resolution images such as detailed satellite imagery, maps and famous paintings.

Database to support infectious disease research

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Linking disease pathogens to clinical signs and symptoms through a database could support research into the molecular mechanisms of infectious diseases.

KAUST Professor Peter Richtárik wins Distinguished Speaker Award

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Peter Richtárik, KAUST professor of computer science, recently received a Distinguished Speaker Award at the Sixth International Conference on Continuous Optimization (ICCOPT 2019) held in Berlin from August 3 to 8. ICCOPT 2019 was organized by the Mathematical Optimization Society and was hosted this year by the Weierstrass Institute for Applied Analysis and Stochastics.

Less chat leads to more work for machine learning

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Deep analysis of the way information is shared among parallel computations increases efficiency to accelerate machine learning at scale.

Machine learning models gather momentum

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New methods for training machine learning models are quicker and more accurate than current approaches, previously considered state-of-the-art.

LD Motif Finder locates ancient hidden protein patterns

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A machine learning method has identified highly elusive amino acid sequences involved in cell morphogenesis and adhesion and in diseases like cancer.

AI uses your imagination

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The psychology of human creativity helps artificial intelligence imagine the unseen.

Approximating a kernel of truth

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Machine learning tasks using very large data sets can be sped up significantly by estimating the kernel function that best describes the data.

Flattening the sentimental curve

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-By Meres J. Weche, KAUST News As municipal, regional and national governments have mandated longer and more restrictive isolation time periods to slow down the spread of the COVID-19 pandemic and "flatten the curve," KAUST Associate Professor of Computer Science Xiangliang Zhang works on flattening the "emotional curve." Zhang's research focuses on developing reliable algorithms to analyze complex data sets through machine learning and artificial intelligence. Her research group specifically uses computer models to analyze social media posts on Twitter to study people's interests. When the

Peeling back the layers of deep machine learning

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A layer-based approach raises the efficiency of training artificial intelligence models.

Tracking the Curve: Analyzing the Emotional Response to COVID-19

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Hashtags like #covid19 and #coronavirus help us stay up to date on the developments of the new coronavirus pandemic. But beyond breaking news, tweets also offer a glimpse into the emotional side of the COVID-19 crisis.

Meet KAUST prospective student: Matheus Vinícius Medeiros de Souza

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Matheus Vinícius Medeiros de Souza is a 24-year-old electrical engineering graduate from the city of Natal, Brazil. Matheus first came to KAUST as a visiting student in 2019 and was instantly impressed by the university’s research facilities.

Meet KAUST Prospective Student: Xinge Yang

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Xinge Yang is a 22-year-old graduate who will join KAUST from the University of Science and Technology of China (USTC). Yang will join the University in the fall of 2020 as an M.S./Ph.D. candidate in the KAUST Computational Imaging Group under the supervision of Professor Wolfgang Heidrich.

Meet KAUST Prospective Student: Igor Sokolov

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Igor Sokolov, 23, is an applied mathematics and physics graduate who will join KAUST from the Moscow Institute of Physics and Technology, Russia. Igor first came to KAUST as a visiting student in 2019 and was highly impressed by the University's living and working facilities. It was this first impression that prompted him to pursue an M.S. at KAUST. Sokolov will join KAUST in the Fall of 2020 as an M.S. candidate under the supervision of Professor Peter Richtarik.

KAUST prospective student: Xiaochuan Gou

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Xiaochuan Gou, 27, from Dalian, China, will join KAUST in the fall of 2020. He will join KAUST as a Ph.D. candidate and member of the Machine Intelligence and kNowledge Engineering research group under the supervision of Professor Xiangliang Zhang.

Meet KAUST prospective student: Zhenwen Liang

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Zhenwen Liang, 22, is a computer science graduate from the University of Electronic Science and Technology of China (UESTC), China. Zhenwen will join KAUST in the fall of 2020 as a M.S./Ph.D. candidate and member of the Machine Intelligence and kNowledge Engineering research group under the supervision of Professor Xiangliang Zhang.

Computers excel in chemistry class

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Machine learning models can rapidly and accurately estimate key chemical parameters related to molecular reactivity.

Meet KAUST prospective student: Kai Yi

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Kai Yi is a graduate from Xi'an Jiaotong University (XJTU), China, who will join KAUST in the fall of 2020. Kai will join KAUST as a M.S./Ph.D. candidate and member of the KAUST Computer Vision, Content AI research group under the supervision of Professor Mohamed Elhoseiny. KAUST's high reputation and Kai's interest in computer vision and machine learning prompted him to choose the University to further his academic career.

Meet KAUST student: Konstantin Burlachenko

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Konstantin Burlachenko holds a master’s degree in computer science from Bauman Moscow State Technical University (BMSTU), Russia. After graduating from BMSTU, he worked in several prominent Moscow companies, such as Huawei, NVIDIA, and Yandex. He joined KAUST in August 2020 as a Ph.D. candidate and member of Professor Peter Richtarik's Optimization and Machine Learning Lab.

Meet KAUST prospective student: Reem Alghamdi

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Reem Alghamdi is a computer science graduate from Princess Nourah bint Abdulrahman University, Saudi Arabia. Reem will join KAUST this fall as a M.S. candidate under the supervision of Professor Peter Richtarik.

Professor Ghanem wins 2020 Abdul Hameed Shoman Award for Arab Researchers

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Bernard Ghanem, KAUST associate professor of electrical and computer engineering and computer science, recently received the 2020 Abdul Hameed Shoman Award for Arab Researchers for ''Machine Learning and Big Data.''

Talented KAUST Ph.D. student aims to revolutionize healthcare with machine learning

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KAUST Ph.D. student, Yu Li, is a talented young computer scientist with an interest in developing novel computational methods and algorithms to solve and understand the principles behind the “bio-world.”

Xin Gao promoted to rank of professor

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Computational Bioscience Research Center (CBRC) would like to congratulate Xin Gao for being promoted to the rank of Professor, effective January 1, 2021. Prof. Gao joined KAUST in October 2010 and became a CBRC PI in January 2013. He has since then produced tremendous work in the areas of computational biology, bioinformatics, machine learning, and algorithms. Dr. Xin Gao is also the Acting Associate Director of the Computational Bioscience Research Center and the lead of the Structural and Functional Bioinformatics Group at KAUST. Prior to joining KAUST, he was a Lane Fellow at Lane Center

Machine learning at speed

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Optimizing network communication accelerates training in large-scale machine-learning models.

Training an AI eye on the Moon

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Machine learning accelerates the search for promising Moon sites for energy and mineral resources.

Drop the zeros for a performance boost

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An eight-fold speed up of deep machine learning can be achieved by skipping the transmission of zero values.

KAUST paper wins 2020 Computational Optimization and Applications Best Paper Award

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KAUST Professor of Computer Science Peter Richtárik and his former student Nicolas Loizou, currently a postdoctoral researcher at Mila - Quebec Artificial Intelligence Institute and soon to take up an assistant professorship position at Johns Hopkins University, recently received the 2020 Computational Optimization and Applications (COAP) Best Paper Award.

Dominik L. Michels appointed Associate Professor of Computer Science and Applied Mathematics

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KAUST faculty member Dominik L. Michels has recently been promoted to associate professor of computer science and applied mathematics.

Computer learns to write captions with feeling

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Mohamed Elhoseiny and his PhD student Youssef Mohamed are teaching AI to capture the full spectrum of human emotions when annotating artwork in order to reduce emotional bias in computer-generated captions.

AI helps diagnose post-COVID lung problems

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A new AI diagnostic tool developed by KAUST scientists allows doctors to visualize lung damage caused by COVID-19 in more detail.

KAUST alumnus named in Forbes' “30 Under 30 Asia list” – Class of 2022

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KAUST alumnus Yu Li (Ph.D. '20) was recently named amongst the Forbes' "30 Under 30 Asia list" – Class of 2022. Forbes recognized the current assistant professor of computer science and engineering at The Chinese University of Hong Kong (CUHK) for his work developing algorithms to solve problems in biology and health care.

Zhize Li

Research Scientist, Computer Science
Zhize Li is a Research Scientist working with Professor Peter Richtarik in his research group at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Zhize Li received his Ph.D. in Computer Science from the Institute for Interdisciplinary Information Sciences of Tsinghua University in 2019 under the supervision of Prof. Jian Li. On the career side, before joining KAUST, he was a visiting scholar at the Computer Science Department of Duke University (Hosted by Prof. Rong Ge) and a visiting scholar at the Industrial and

Zhuo Yang

Ph.D. Student, Computer Science
Zhuo Yang is a Ph.D. student in Computer Science program at Machine Intelligence and kNowledge Engineering (MINE) Laboratory under the supervision of Professor Xiangliang Zhang at King Abdullah University of Science and Technology (KAUST). Research Interests Zhuo's research interests include machine learning and data mining. Education Profile ​B. E., Computer Science, Nankai University, China, 2014. M. E., Computer Science, Nankai University, China, ​2017.

CEMSE New Faculty: Professor Rolf Krause

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Professor Rolf Krause, our new faculty member at KAUST, drives sustainable progress across various domains, from medicine to geology, with his pioneering research in applied mathematics and efficient algorithms.

Zhenwen Liang

Ph.D. Student, Computer Science
Zhenwen Liang is a M.S./Ph.D. candidate in the KAUST Machine Intelligence and kNowledge Engineering research group under the supervision of Professor Xiangliang Zhang. Before joining KAUST, Zhenwen obtained a bachelor's degree in computer science from the University of Electronic Science and Technology of China (UESTC), China. Research Interests Zhenwen's research interests include AI research and machine learning.

Zihang Xiang

Ph.D. Student, Computer Science
Zihang Xiang is a Ph.D. student at ‌Privacy-Awareness, Responsibility and Trustworthy Laboratory under the supervision of Professor Di Wang at King Abdullah University of Science and Technology (KAUST). Publications Z. Xiang, M. M. Khan, U. Khalid and J. Xu, ”A DC link capacitors’ voltage balancing method in unidirectional cascaded H-bridge converter using multi-limb-core (MLC) transformers,” 2017 China International Electrical and Energy Conference (CIEEC),Beijing, 2017, pp. 606-611. U. Khalid, M. M. Khan, Z. Xiang and Y. Jianyang, ”Bidirectional modular Dual Active Bridge (DAB) converter

Yuxi Hong

Ph.D. Student, Computer Science
Yuxi Hong is a PhD student working with Professor David Keyes. He is working on accelerating the training process of deep learning from different aspects. Why KAUST? KAUST is a great place for research. Why HPC and machine learning? I believe we need more computation in the future. Scalable and efficient computing methods are basic tools for the scientific research. Education Profile MS Tsinghua University. BS Tsinghua University.

Youssef S. Mohamed

Ph.D. Student, Computer Science
Education Youssef Mohamed is a Computer Science Ph.D. candidate at the Visual Computing Center (VCC)/Vison-CAIR at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Mohamed Elhoseiny Bachelor's Degree in Communication and Information Engineering, University of Science and Technology in Zewail City, Cairo, Egypt (2013-2018) Master's Degree in Computer Science, University of Tartu, Tartu, Estonia (2019-2022) Research Interests I am interested in developing AI that is aware of the Emotional and Cultural aspects of human intelligence. We study human

Yuchen Li

Ph.D. Student, Computer Science
Yuchen Li is a PhD Student under the supervision of Professor Mohamed Elhoseiny in Vision-CAIR Group at Visual Computing Center at King Abdullah University of Science and Technology (KAUST). Education and Early Career Before joining KAUST, Yuchen was a research intern at iFLYTEK, an intelligent speech and artificial intelligence company in Hefei, China, for three months. He was a Rocket MQ open source contributor and Alibaba summer of code student developer with rich research and engineering experience. He visited NUS, UBC, THU, and other prestigious universities for academic exchanges with a

Yulian Wu

Ph.D. Student, Computer Science
Yulian Wu is a Ph.D. student under the supervision of Professor Francesco Orabona at King Abdullah University of Science and Technology (KAUST). Publications Youming Tao*, Yulian Wu*, Peng Zhao and Di Wang. "Optimal Rates of (Locally) Differentially Private Heavy-tailed Multi-Armed Bandits." https://arxiv.org/abs/2106.02575. (Equal contributions) Education Profile M.S. in Statistics, East China Normal University, China B.S. in Mathematics and Applied Mathematics, East China Normal University, China

Yipeng Qin

Postdoctoral Research Fellow, Computer Science
Yipeng Qin was a Postdoctoral Research Fellow working with Professor Peter Wonka at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Currently, he is a Lecturer at the School of Computer Science and Informatics in Cardiff University (CU), United Kingdom. Education and Early Career In 2013, Yipeng Qin graduated with a Bachelor of Engineering in Electronic Engineering from Shanghai Jiao Tong University in China. Later during 2017, he obtained his Ph.D. in Computer Science from the National Centre for Computer Animation, Bournemouth University in

Yu Li

Ph.D. Student, Computer Science
Research Interests Yu Li is a Ph.D. student at KAUST, majoring in Computer Science. His advisor is Prof. Xin Gao. His research interests are Deep Learning, Machine Learning, and Bioinformatics. He got his Master degree in Computer Science at KAUST in December 2016. Before that, he got his Bachelor degree in Biosciences at the University of Science and Technology of China (USTC). Scientific and Professional Membership Association for the Advancement of Artificial Intelligence(AAAI) Member International Society for Computational Biology (ISCB) Member Education Profile Ph.D student, Computer

Yazeed Alharbi

Yazeed Alharbi is a Ph.D. candidate under the supervision of Prof. Peter Wonka at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career In 2015, Yazeed obtained his bachelor degree from Purdue University in the computer graphics and visualization track, with a minor in philosophy. In 2018, he received his master degree from King Abdullah University of Science and Technology (KAUST). He mainly learned about computer vision and the process of publication in that field. Research Interest Currently, Alharbi research is focused

Yang Liu

Ph.D. Student, Computer Science
Yang Liu is an M.S./Ph.D. candidate in the KAUST Bio-Ontology research group under the supervision of Professor Robert Hoehndorf. Before joining KAUST. Research Interests Yang's interests include bioinformatics, machine learning and human genomics. She is interested in building models for human disease diagnosis and prognosis, especially for cancer, and applying statistical methods to analyze human omics data. Education Profile B.Sc, Bioengineering, Harbin Institute of Technology, Harbin, China, 2016-2020. M.Sc, Bioengineering, King Abdullah University of Science and Technology, Thuwal, Saudi

Yahya Bokhari

Postdoctoral Research Fellow, Computer Science
Yahya Bokhari is a Postdoctoral Research Fellow at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST), working with Professor Wolfgang Heidrich on enhancing X-ray images under Smart Health Initiative. Education and Early Career Yahya Bokhari received his bachelor's degree in Medical Technology from King Abdulaziz University, KSA. He holds a Master's degree in Bioinformatics and a Ph.D. degree in Computer Science from Virginia Commonwealth University, USA. Research Interest Dr. Yahya Bokhari is mainly interested in Image Processing, Computer Vision

Xin Gao

Program Chair, Computer Science
Professor Gao is a world-leading expert in developing novel AI solutions for challenges in biology, biomedicine, and health and wellness, with a focus on AI-based drug development, large language models in biomedicine, biomedical imaging analysis, and omics-based disease detection and diagnostics.

Xiaochuan Gou

Ph.D. Student, Computer Science
Xiaochuan Gou is a Ph.D. candidate in the KAUST Machine Intelligence and kNowledge Engineering research group (MINE) under the supervision of Professor Xiangliang Zhang. Before joining KAUST, Xiaochuan obtained his master’s degree in Computer Science from National Chiao Tung University and a bachelor’s degree in Spatial Information and Digital Technology from Wuhan University, China, respectively. Research Interests Xiaochuan's research interests include machine learning, data mining, and GIS. He aspires to build new machine learning models to resolve existing problems in public traffic

Xi Peng

Ph.D. Student, Computer Science
Xi Peng likes pop music and doing sports. He graduated from SUSTech in 2021 and planning to continue his academic career at KAUST under the supervision of Professor Robert Hoehndorf. "He says KAUST has the best environment to do research. Life in KAUST is also comfortable. I believe I can achieve a lot in KAUST". Research Interest applications of machine learning and logic integration in biology Education Profile B.Sc, Computer Science, Southern University of Science and Technology, Shenzhen, China, 2017-2021 M.Sc, Computer Science, King Abdullah University of Science and Technology, Thuwal

Trisevgeni Rapakoulia

Trisevgeni Rapakoulia obtained her Ph.D. degree (2016-2019) in Computer Science under the supervision of Professor Xin Gao at structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Research Interests Trisevgeni's research interests include bioinformatics, System Biology, Prediction of harmful SNPs, Genome-Wide Association Studies and Artificial Intelligence, Machine Learning Approaches, Evolutionary Algorithms, Feature Selection Techniques. Research field: study the effects of drugs and drug combinations in the transcriptome level

Vasiliki Kordopati

Vasiliki Kordopati obtained Master degree in Computer Science under the co-supervision of Professor Boon Ooi at Photonics Laboratory and Prof. Xin Gao at Structural and Functional Bioinformatics Group (SFB) from King Abdullah University of Science and Technology (KAUST). Vasiliki is a co-founder of Oæsis, a King Abdullah University of Science and Technology (KAUST) startup spinout, where she is responsible for product development. A computer engineer by training, she received a bachelor's degree in Computer Engineering from the University of Patras, Greece. Research Interest Vasiliki's

Uchenna Akujuobi

Uchenna Akujuobi obtained his Ph.D. degree in computer science under the supervision of Prof. Xiangliang Zhang leading the MINE group at King Abdullah University of Science and Technology (KAUST). Research interests Uchenna's research interests included Machine learning, data mining, Social Networks, Graph mining, Information Retrieval, Text Mining, Big Data, and Deep Networks. Ph.D. dissertation entitled “Learning from Scholarly Attributed Graphs for Scientific Discovery”​. First employment: Research Scientist, Sony AI​, Tokyo, Japan. He is also the creator of Delve, a dataset retrieval, and

Ussama Zahid

Ph.D. Student, Computer Science
Ussama Zahid is a PhD student in the Accelerated Connected Computing Lab at KAUST. He works at the intersection of deep learning and embedded systems, i.e., “Edge AI”. He specializes in efficient training and inference of quantized deep neural networks (DNNs) and hardware-software co-design of DNN accelerators. Before joining KAUST, he was as a research engineer intern at Xilinx Research Labs, Dublin Ireland, Ireland where he worked on quantized and robust training of Deep Neural Networks (DNNs), resulting in patents and publications. He received his Bachelor's degree in Electrical Engineering

Tilman Hinnerichs

Visiting Student, Bio-Ontology Research Group
Tilman Hinnerichs was a visiting student at Professor Robert Hoehndorf's Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Research Interests Tilman's research interests included ​Machine Learning, Bioinformatics, Ontologies, Reasoning, Logic, Particle Methods. He was particularly interested in the application of machine-learning algorithms to problems of the bioscience field. Education Profile ​Since 2016: Diploma Student, Computer Science, TU Dresden​. 2015-2016: Student, Human Medicine, Charité Berlin.

Su Wang

Su Wang obtained a master's degree in Computer Science under the supervision of Professor Xiangliang Zhang at Machine Intelligence and kNowledge Engineering (MINE) Laboratory in King Abdullah University of Science and Technology (KAUST). First employment: Didi Chuxing​, Beijing, China, 2016. Research Interests Su's research interests included ​Data mining and machine learning. Education Profile ​​B.Sc., Information Science & Electronic Engineering, Zhejiang University, 2014. M.Sc., Computer Science, King Abdullah University of Science and Technology (KAUST). Awards and Distinctions First Prize

Somayah Albaradei

Ph.D. Student, Computer Science
Somayah Albaradei is a Ph.D. student at Structural and Functional Bioinformatics Group (SFB) under the supervision of Professor Xin Gao at King Abdullah University of Science and Technology (KAUST). ​Somayah obtained her master's degree in computer science from the University of Manitoba, Canada in 2014. Professional Profile 2014: Lecturer at College of Computing and Information Technology-King Abdulaziz University - Jeddah - Saudi Arabia 2010- 2011: Teacher Assistant at College of Computing and Information Technology-King Abdulaziz University - Jeddah - Saudi Arabia 2009-2010: Collaborator

Siqing Hou

Siqing Hou obtained his Master's degree in Computer Science under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Master Thesis, 2018: Neural Inductive Matrix Completion for Predicting Disease-Gene Associations Research Interests Siqing Hou was working in the fields of Bioinformatics, Computational Biology and Machine Learning. Professional Profile Research Student, University of Birmingham, Jul 2015 - Aug 2018 Software Effort Estimation, Machine Learning Education Profile M.Sc. Computer

Shichao Pei

Ph.D. Student, Computer Science
Shichao Pei is a Ph.D. student in Computer Science program at Machine Intelligence and kNowledge Engineering (MINE) Laboratory under the supervision of Professor Xiangliang Zhang at King Abdullah University of Science and Technology (KAUST). Research Interests Shichao Pei research interests include machine learning and data mining. Education Profile ​B. E., Software Engineering, Xi'an Jiaotong University​, 2016.

Shahid Hussain

Ph.D. Student, Computer Science
Research Interests Design and analysis of decision trees based on dynamic programming and greedy approaches Machine learning Discrete optimization Selected Publications Azad, M., Chikalov, I., Hussain, S., and Moshkov, M. Restricted multi-pruning of decision trees, In Machine Learning and Data Analytics Symposium as plenary talk (2016) (Awarded best paper). Busbait, M., Chikalov, I., Hussain, S., and Moshkov, M. Diagnosis of constant faults in read-once contact networks over finite bases. Discrete Applied Mathematics, 184 (2015), pp. 76–88. AbouEisha, H., Hussain, S., Lozin, V., Monnot, J

Shariq Farooq Bhat

M.S. Student, Computer Science
Shariq Farooq Bhat is an MS/PhD student under the supervision of Professor Peter Wonka at King Abdullah University of Science and Technology (KAUST). Education and Early Career Shariq Farooq Bhat obtained his bachelor of technology degree in Electronics & Communication Engineering from the National Institute of Technology Srinagar, India in 2018. During bachelor’s degree, he was an intern at Texas Instruments (TI) Center for Embedded Product Design at NSIT in New Delhi. Then, in 2016, he worked at the Indian Institute of Science (IISc) as a computer vision and deep learning research Intern and

Sheng Wang

Sheng Wang worked as a Research Scientist at Prof. Xin Gao's Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Sheng got his Ph.D. in Bioinformatics from the Institute of Theoretical Physics at the Chinese Academy of Sciences in 2010. He was then a postdoc fellow at Toyota Technological Institute in Chicago during 2010 and 2014. During 2014 and 2017, he was a research professional at the Department of Human Genetics at the University of Chicago. Since March 2017, he joined the SFB group as a research scientist. Currently, Sheng is a

Shahad Qathan

M.S. Student, Computer Science
Shahad Qathan is an MS candidate in the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Robert Hoehndorf. Research Interests Artificial Intelligence, more specifically Machine Learning and its applications in healthcare and Bioinformatics Education Profile B.Sc, Computer Science, King Abdulaziz University, Jeddah, Saudi Arabia, 2020

Sara Althubaiti

Ph.D. Student, Computer Science
Sara Althubaiti is currently a Ph.D. candidate in the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Robert Hoehndorf. Prior to this, Sara received her master's degree in computer science with a focus on bioinformatics from KAUST in December 2018. Research Interests Sara's research interests include bioinformatics, text mining, ontologies, and cancer. Her research focuses on applying machine learning methods in cancer biology and development specifically in the field of finding driver genes and mutations in

Sakhaa Alsaedi

Ph.D. Student, Computer Science
​Sakhaa Al-Saedi completed her bachelor's degree in Computer Science in 2016 at Taibah University. She is the founder of the Medvation startup company, inventing educational kits that teach children concepts of robotics and Machine Learning (ML) in fun ways. She worked as a product developer at the Namma Al-Munawara company, Madinah, before starting her master's degree at KAUST in 2017. She is currently working with the Bio-Ontology Research Group (BORG) of Professor Robert Hoehndorf on human genome sequencing to evaluate the impact of Saudi-specific allele frequencies on variant calling

Ricardo Henao

Associate Professor (former), Bioengineering
​Ricardo Henao, a quantitative scientist, is an Associate Professor of Bioengineering in the Biological and Environmental Science and Engineering ( BESE) Division, and Computer Science in the Computer, Electrical, Mathematical Sciences and Engineering Division ( CEMSE), member of the Smart Health Initiative ( SHI) and the Computational Biosciences Research center ( CBRC), at KAUST (King Abdullah University of Science and Technology). He is also currently an Associate Professor in the department of Biostatistics and Bioinformatics, Department of Electrical and Computer Engineering ( ECE)

Renmin Han

Postdoctoral Research Fellow, Structural and Functional Bioinformatics
Renmin Han is a Research Scientist at the Structural and Functional Bioinformatics Group (SFB) of Professor Xin Gao at King Abdullah University of Science and Technology (KAUST). ​Renmin obtained his Ph.D. degree in Computer Science from the Institute of Computing Technology, Chinese Academy of Sciences, China. Research Interests ​Renmin is interested in the development of computer science. Currently, he is working on imaging processing in electron micrography, especially for the reconstruction of biological ultrastructures. His work is to use computational methods to improve the boundary of

Ramzan Umarov

Ph.D. Student, Computer Science
Ramzan is a PhD candidate, working on solving biological problems by using applied machine learning with focus on deep learning. He has developed deep learning based methods to solve bioinformatics problems achieving state-of-the-art performance, focusing on various aspects of gene regulation. Umarov obtained his Master degree from Imperial College London, Advanced Computing course. Research Interests The main research interest of Ramzan Umarov is applied machine learning especially Deep Learning. Professional Profile 2011-2012, Programmer, Softberry, Mount Kisko, NY, USA 2013-2014, Programmer

Rawan Olayan

Rawan Olayan obtained both, her MS Degree and PhD Degree from Computer Science at KAUST in 2013 and 2018 respectively. Her master thesis and PhD thesis supervisor was Vladimir Bajic. Under the supervision of her advisor Professor Vladimir Bajic, she completed her thesis on developing novel computational methods to predict drug-target interactions and their functional effects using data integration, graph mining and machine learning approaches. Currently, Rawan is working as a postdoctoral associate in the Carter Lab at the Jackson Laboratory for Genomic Medicine in Farmington, Connecticut. In

Rahaf Shiqdar

Visiting Student, Bio-Ontology Research Group
Rahaf Shiqdar was a visiting student from ​Umm Al-Qura University ​at Professor Robert Hoehndorf's Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST). Research Interests Rahaf's research interests included ​Bioinformatics, Machine and Deep learning.

Rameen Abdal

Ph.D. Student, Computer Science
Rameen Abdal is a Master student under the supervision of Professor Peter Wonka at King Abdullah University of Science and Technology (KAUST). He has graduated in 2023. Education and Early Career Rameen Abdal obtained his bachelor of technology degree in Electronics and Communications Engineering from the National Institute of Technology in Srinagar, India in 2018. After that, he joined KAUST for the MS/PhD program in computer science to continue his studies. Before joining KAUST, Rameen was an intern at Texas Instruments (TI) Center for Embedded Product Design at Netaji Subhas University of

Peter Wonka

Professor, Computer Science
Professor Peter Wonka is a computer scientist with a research focus in computer vision, computer graphics, remote sensing, and machine learning.

Qiannan Zhang

Ph.D. Student, Computer Science
Qiannan Zhang is a Ph.D. student in Computer Science program at Machine Intelligence and Knowledge Engineering (MINE) Laboratory under the supervision of Professor Xiangliang Zhang at King Abdullah University of Science and Technology (KAUST). ​​​​​Research interests Qiannan's research interests include Machine learning and data mining​. Education Profile B. E., Communication Engineering, Tianjin University, China, 2010-2014. M. E., Communication Engineering, Tianjin University, China, 2014-2017. Research Intern, KAUST, 2017.

Peter Richtarik

Professor, Computer Science
Professor Richtárik specializes in big data optimization and machine learning. He is known for his work on randomized coordinate descent algorithms, stochastic gradient descent and federated learning.

Peihao Zhu

Ph.D. Student, Computer Science
Peihao Zhu is a Computer Science Ph.D. candidate at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Peter Wonka in his research group. He has completed his Ph.D and graduated in 2023. Education and Early Career Peihao Zhu obtained his bachelor of engineering in Automation from Northeastern University in China in 2016. After that, he continued his studies in Computer Science and received his master's degree from King Abdullah University of Science and Technology (KAUST) in Saudi Arabia in 2019. Research Interest

Peng Yang

Postdoctoral Research Fellow, Structural and Functional Bioinformatics
Peng Yang worked as a Postdoctoral research fellow at Prof. Xin Gao's Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST) during 2016-2018. Currently, Peng is a research scientist at Baidu Research. Research Interests ​Machine Learning and Data Mining problem, especially online active learning, multi-task learning, recommender systems, and deep learning. Algorithms for bioinformatics problems, especially in disease gene prediction, drug-target interaction prediction, protein complex, and microbial community pattern detection

Olaa A. Motwalli

Research Interests Olaa Motwalli is a Ph.D. student in Computational Science and a member of the Computational Bioscience Research Center (CBRC) at KAUST. She obtained her Bachelor's degree in Computer Science at King Abdul Aziz University in Jeddah and obtained her Master's degree in Computer and Software Engineering at Widener University in the USA. Her research interests are mainly the development and computational support for the design of sustainable biofuel production via microbial cell factories through machine learning, graph mining, and algorithm development, genome-scale metabolic

Omar Mena Facio

M.S. Student, Computer Science
Omer Mena Facio is a Master student in Computer Science under the supervision of Professor Ivan Viola at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career Omer Mena Facio

Noor Bafageeh

Noor Bafageeh was a Master student in Computer Science Program under the supervision of Professor Bernard Ghanem at Visual Computing Center (VCC) at King Abdullah university of Science and Technology (KAUST). Education and Career Noor earned her bachelor degree in Computer Science from Effat University in Jeddah in 2016. She joined KAUST and received her master degree in Computer Science in 2018. Research Interest She is interested in computer vision, machine learning, Generative Adversarial Networks (GAN) and deep learning. Education Profile M.S., Computer Science, King Abdullah University of

Ngan Nguyen

Ph.D. Student, Computer Science
Ngan Nguyen is a Ph.D. candidate in the Computer Science Program under the supervision of Professor Ivan Viola in the Nanovisualization Research Group at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Ngan has a Master's Degree in Computer Science. She worked at Gameloft Company as a Developer. After that, she joined the University of Information Technology, Vietnam National University in Ho Chi Minh City as a Lecturer. Research Interest Ngan is focusing on Computer Graphics, Visualization, and their overlaps into

Norah S. Alballa

Ph.D. Student, Computer Science
Education Profile Ph.D. in Computer Science, CEMSE, KAUST, 2022-present M.S. in Computer Science, Portland State University, 2016 B.S. in Information Technology, King Saud University (KSU), 2010

Mona Alshahrani

Mona Alshahrani obtained her Ph.D. degree in Computer Science under the supervision of Professor Robert Hoehndorf at the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST). Research Interests Machine Learning, human bioinformatics, data integration. The combination of neuro-symbolic methods over ontologies and semantic RDF graphs with statistical methods. Their applications in data analysis and features representations in several biomedical applications such as phenotypes prediction, drug repurposing, and others. I'm also interested in network

Mohammad Mohiuddin Azad

Ph.D. Student, Computer Science
Research Interests Study of greedy and dynamic programming algorithms for construction of exact and approximate decision trees, rules and reducts for decision tables with many-valued decisions Analysis and design of classifiers based on decision trees, reducts, and decision rule systems Data mining, Machine Learning Selected Publications Mohammad Azad, Igor Chikalov, Shahid Hussain and Mikhail Moshkov. “Restricted multi-pruning of decision trees”. MLDAS (Machine Learning and Data Analytics Symposium) 2016 organized by QCRI (Qatar Computing Research Institute) and Boeing International, Doha

Modar Alfadly

Ph.D. Student, Computer Science
Modar Alfadly is a Ph. D. candidate in Image and Video Understanding Lab (IVUL) Group under the supervision of Prof. Bernard Ghanem at KAUST. Education and Early Career Alfadly received his bachelor’s degree in Software Engineering from King Fahd University of Petroleum and Minerals (KFUPM) in 2016. A year later, he obtained his master’s degree in Computer Science and successfully defended his thesis, titled “Analytic Treatment of Deep Neural Networks under Additive Gaussian Noise”. Research Interest Modar Alfadly is focusing on understanding deep neural networks to build more intelligent

Merey Ramazanova

Ph.D. Student, Computer Science
Merey Ramazanova is a Computer Science Ph.D. Student in Image and Video Understanding Lab (IVUL) Group at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Bernard Ghanem. Education and Early Career Merey obtained her bachelor's degree in Computer Science from Nazarbayev University in Kazakhstan in 2018. She also was a Research Intern at Okinawa Institute of Science and Technology (OIST) in Okinawa, Japan in 2018. Research Interest She is focusing on her research on data science, computer vision, and machine learning. Education Profile M.S., Computer

Mikhail Moshkov

Professor Moshkov specializes in various areas of discrete applied mathematics, theoretical computer science and operations research. He is the author or co-author of more than 200 research publications, including 10 books published by Springer.

Matheus Vinícius Medeiros de Souza

Ph.D. Student, Computer Science
Matheus Vinícius Medeiros de Souza is an MS/Ph.D. candidate in the KAUST Computational Imaging Group under the supervision of Professor Wolfgang Heidrich. Before joining KAUST, Matheus obtained a bachelor's degree in electrical engineering from the Federal University of Rio Grande do Norte, Brazil. Research Interests Matheus's research interests include Computer Vision and Machine learning.

Maxat Kulmanov

Research Scientist, Bio-Ontology Research Group
Maxat Kulmanov is a Research Scientist in the Bio-Ontology Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Maxat's research interests include bioinformatics, knowledge representation and reasoning, machine learning, neural networks, semantic web, and algorithms. He is interested in knowledge discovery and data integration using artificial intelligence and semantic web technologies in biology and biomedicine. Professional Profile 2020-Present: Postdoctoral Research Fellow, Computational Bioscience Research Center, King Abdullah University of

Masheal Alghamdi

Masheal Alghamdi is a Ph. D. candidate in the Computational Imaging Group (VCCIMAGING) under the supervision of Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Masheal obtained her bachelor degree in Computer Science from King Abdul-Aziz University (KAU) in Jeddah, Saudi Arabia in 2009. After that she joined KAUST to receive her master degree in Computer Science in 2014. Masheal is a researcher in the National Center for Computation Technology and Applied Math (CTAM) at King Abdul-Aziz City for Science and Technology (KACST)

Majed Alzahrani

Majed Alzahrani obtained PhD degree in Computer Science from King Abdullah University of Science and Technology in 2017. His PhD thesis supervisor was Professor Xin Gao. After graduating, he joined Saudi Aramco as a data scientist in its Drilling Data Science unit. Working in one of the largest companies in the world is a dream come true for many, for Majed Alzahrani, things couldn't be better. Research Interest Majed's research interests were in machine learning, structural bioinformatics, synthetic biology.

Marco Canini

Associate Professor, Computer Science
Professor Canini’s research seeks to improve networked-system design, implementation and operation concerning vital properties such as reliability, performance, security and energy efficiency.

Maha Thafar

Ph.D. Student, Computer Science
Research Interests I am a computer scientist focusing on developing novel computational methods using artificial intelligence, machine learning, deep learning, and graph mining techniques to solve biomedical problems. I am developing these methods to be applied to large-scale chemical and biomedical data. Currently, I'm working on compound-protein interactions prediction and binding affinity prediction projects. My objective is to empower women in my country with a flourishing academic background that enables building better careers for themselves. Additionally, to create a research culture

Lu Yu

Lu Yu obtained his Ph.D. degree in computer science under the supervision of Prof. Xiangliang Zhang leading the MINE group at King Abdullah University of Science and Technology (KAUST). He mainly worked on learning representation of discrete data structure (e.g. graph, sequence, entity) that intersected with graph theory, optimization, adversarial learning. Before joining KAUST, he worked as a Data Mining Engineer at Alibaba Group. Ph.D. dissertation entitled “Exploring Entity Relationship in Pairwise Ranking: Adaptive Sampler and Beyond”. First employment: Senior Algorithm Engineer, Ant Group

Lijie Hu

Ph.D. Student, Computer Science
Lijie Hu is a Ph.D. student at ‌Privacy-Awareness, Responsibility and Trustworthy Laboratory under the supervision of Professor Di Wang at King Abdullah University of Science and Technology (KAUST). Publications Lijie Hu, Shuo Ni, Hanshen Xiao and Di Wang. "High Dimensional Differentially Private Stochastic Optimization with Heavy-tailed Data". https://arxiv.org/abs/2107.11136. To appear in the 41st ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems (PODS 2022). Di Wang*, Jiahao Ding*, Lijie Hu, Zejun Xie, Miao Pan and Jinhui Xu. "Differentially Private Expectation

Lizhong Ding

Postdoctoral Research Fellow, Structural and Functional Bioinformatics
Lizhong Ding worked as a Postdoctoral research fellow at Prof. Xin Gao's Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST) during 2016-2018. Currently, Lizhong is a research scientist at the Inception Institute of Artificial Intelligence. Research Interests Machine learning theory Impact of approximation on learning algorithms, especially discrepancy bounds between approximate and accurate learning, for kernel learning and deep learning Large scale machine learning Large scale matrix analysis/approximation Scalable model

Lama Alssum

Ph.D. Student, Computer Science
Lama Alssum is a Ph.D. student in Computer Science under the supervision of Professor Bernard Ghanem in Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Lama studied Information technology at King Saud University (KSU) in Riyadh and graduated with a Bachelor degree in 2011. After her bachelor degree, she joined KSU as a Teaching Assistant. Later on, she continued her studies and earned her Master degree in Computer Science from the School of Computing Science at the University of Glasgow, United Kingdom. Lama first joined

Li Zhou

Ms/PhD Student, CTF player, Interested in Reverse Engineering

Kumail Alhamoud

M.S. Student, Computer Science
Kumail Alhamoud is a Computer Science M.S Student at KAUST working under the supervision of Professor Bernard Ghanem at the Image and Video Understanding Lab (IVUL). Before joining KAUST, Kumail obtained his B.S in Electrical and Computer Engineering at Cornell University in 2021. His current research interests include continual learning, learning from limited data under distribution shifts, and AI for Science and Biomedicine. Publications

Konstantin Mishchenko

Ph.D. Student, Computer Science
Konstantin Mishchenko is a Ph.D. candidate at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Richtarik in his research group. Education and Early Career Konstantin Mishchenko obtained his bachelor degree in Computer Science and Physics from Moscow Institute of Physics and Technology in 2016. After that in 2017, he received his master degree in Machine Learning MASH from Université Paris Dauphine in Paris, France. Konstantin had many internships during his studies. The most recent was with Amazon in

Kai Yi

Kai Yi is a Ph.D. candidate in Optimization and Machine Learning Lab led by Professor Peter Richtarik. He received his Computer Science MS from KAUST in Dec. 2021. Before that, he received B.Eng with honor from the Department of Software Engineering, Xi’an Jiaotong University, in June 2019. He has interned at Tencent AI Lab, CMU, NUS, and SenseTime. Research Interest Kai Yi's current research interests include Machine learning optimization in the large-scale data/model era. Conceptual-level knowledge transfer learning: theories and applications. Specifically, he works on machine learning

Jürgen Schmidhuber

Professor, Computer Science
Professor Schmidhuber is a renowned computer scientist, best known for his pioneering work in artificial intelligence (AI), deep learning and artificial neural networks. He has authored more than 350 peer-reviewed papers and has provided AI strategies to several governments.

Jun Chen

Ph.D. Student, Computer Science
Jun Chen is a Ph.D. candidate in the Computer Science Program under the supervision of Professor Mohamed Elhoseiny at King Abdullah University of Science and Technology (KAUST). Education and Early Career Jun obtained his bachelor's degree in Computer Science from Xi’an Jiaotong-Liverpool University in China, 2018. He joined KAUST as a Master student under the supervision of Prof. Robert Hoehndorf and received his master's degree in Computer Science in 2019. Research Interests Jun's research interests include machine learning, artificial intelligence, natural language processing, knowledge

Jiayao Zhang

M.S. Student, Computer Science
Jiayao Zhang is a Computer Science Master's student in the MS/Ph.D. program under the supervision of Professor Dominik Michels at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia. Education and Early Career Jiayao completed her Bachelor's degree from the Chongqing University of Posts and Telecommunications. She was working with Prof. Chenqiang Gao on Event Detection and recognition topics. She was a visiting student at Shanghai Jiao Tong University (SJTU) in China with Prof. Cewu Lu in the MVIG lab and also worked as a Research

Jay Morgan

Visiting Student, Computer Science
Jay Morgan was a visiting student at (BORG) Bio-Ontology Research Group of Professor Robert Hoehndorf at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. ​Research Interests Jay's research is focused on the verification of machine learning (ML) models, in particular, deep learning. Reasoning as to the decision process of ML is becoming increasingly important especially those that are included in safety-critical systems. Therefore, verifying the properties of the model against requirements as well as interrogating the model itself could provide a great benefit.​​ His

Jesper Tegner

Professor, Bioscience
Jasper Tegner is an M.D./Ph.D., innovator and professor with over 350 publications, dedicated to exploring the interconnected mechanisms underlying life, intelligence and the mind. His research aims to advance fundamental progress in artificial intelligence by moving beyond engineering to understand the intrinsic modes of operation within cells, between cells and within the brain.

Ismail AlQerm

Ismail AlQerm obtained his Ph.D. degree in Computer Science under the supervision of Professor Basem Shihada at King Abdullah University of Science and Technology (KAUST). Currently, he is working as a Post-Doctorate Fellow at the University of Missouri-St. Louis, USA. Research Interests Ismail's research interests included Cognitive radios, Resource Allocation, Machine Learning, Cellular Networks, and HetNets. Thesis title: Novel Machine Learning-Based Techniques for Efficient Resource Allocation in Next Generation Wireless Networks. Academic Advisors Basem Shihada, Professor, Computer

Imane Boudellioua

Imane Boudellioua obtained her Ph.D. degree in Computer Science under the supervision of Professor Robert Hoehndorf at the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST). Imene has a Master's degree in Computer Science from KAUST. During Master's her thesis supervisor was Professor Basem Shihada. Thesis title: " A Vehicular Guidance Wireless Sensor Actuator Network." Research Interests Imane Boudellioua's research interests include the application of machine learning and data mining algorithms for functional annotation of various biological

Ibrahim Alabdulmohsin

Ph.D. Student, Computer Science
Ibrahim Alabdulmohsin obtained a Ph.D. degree in Computer Science under the co-supervision of Professor Xiangliang Zhang at Machine Intelligence and kNowledge Engineering (MINE) Laboratory and Professor Xin Gao at Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Currently, Ibrahim is working with IT Strategy and Technology Planning Department at Saudi Aramco. Pursued his Ph.D. degree at MINE Lab from 2012 - 2017. Ibrahim obtained his M.Sc. degree in Computer Science from Stanford University. Dissertation Title: Learning via Query

Igor Chikalov

Senior Research Scientist, Computer Science
Research Interests Application of dynamic programming to building decision trees and decision rules Machine learning Design of algorithms and complexity analysis Selected Publications I. Chikalov, V. Lozin, I. Lozina, M. Moshkov, H.S. Nguyen, A. Skowron, B. Zielosko, Three Approaches to Data Analysis: Test Theory, Rough Sets and Logical Analysis of Data, Intelligent Systems Reference Library, Vol. 41, Springer, 2012 I. Chikalov, Average Time Complexity of Decision Trees, Intelligent Systems Reference Library, Vol. 21, Springer, 2011. I. Chikalov, P. Yao, M. Moshkov, J.C. Latombe, Learning

Ibrahim Abdelaziz

Ibrahim Abdelaziz obtained his Ph.D. degree in Computer Science under the supervision of Professor Panagiotis Kalnis from King Abdullah University of Science and Technology (KAUST). Research Interests Ibrahim Abdelaziz's research interests included Data Mining over large datasets, Distributed Systems, Machine Learning and Pattern Recognition. Selected Publications Using Advanced Hidden Markov Models for online handwriting recognition. Ibrahim Hosny, Sherif Abdou, Aly Fahmy In Proceeding of Asian Conference of Pattern Recognition (ACPR), (2011) Large Vocabulary Arabic Online Handwriting

Humam Alwassel

Ph.D. Student, Computer Science
Humam Alwassel is a Computer Science Ph.D. candidate in Image and Video Understanding Lab (IVUL) Group under the supervision of Professor Bernard Ghanem at King Abdullah University of Science and Technology (KAUST). Education and Early Career Humam obtained his bachelor degree with double major in Computer Science and Mathematics from Cornell University in New York, USA in 2016. After that, he joined KAUST for the MS/PhD program and received his master degree in Computer Science in 2018. Research Interest He’s currently focused on the development of novel computer vision techniques for video

Haitham M. Ashoor

Research Interests Haitham Ashoor is a Ph.D. candidate in the Computer Science program at King Abdullah University of Science and Technology (KAUST). He is working under the supervision of Prof. Vladimir Bajic. He has obtained his BSc degree in Computer Engineering from the University of Jordan, Amman Jordan in 2008. He joined KAUST as an MSc student in 2009. In 2011, He finished his master studies under the supervision of Prof. Vladimir Bajic. His main research interests are developing computational methods for next-generation sequencing data analysis and applications of machine learning in

Guolei Sun

Guolei Sun obtained his MS degree in the Computer Science program at Machine Intelligence and kNowledge Engineering (MINE) Laboratory under the supervision of Professor Xiangliang Zhang at King Abdullah University of Science and Technology (KAUST). Research interests Guolei's research interests include Machine learning and data mining. Education Profile ​B.E., Biomedical Engineering, Huazhong University of Science & Technology​, 2015​. M.Sc., Computer Science, King Abdullah University of Science and Technology (KAUST).

Gian Marco Messa

Ph.D. Student, Computer Science
Gian Marco is a Ph.D. student at the Structural and Functional Bioinformatics Group (SFB) under the supervision of Professor Xin Gao at King Abdullah University of Science and Technology (KAUST). Gian Marco, upon finishing high school in Lecce (Italy), applied to the Politecnico of Milan in the year 2009 in Physics engineering. Due to personal issues, he came back to his hometown to complete the Bachelor's Degree at the University of Salento in Biotechnology. For his master's degree, seeking innovation and cutting edge knowledge, he moved to the University of Bologna, which offered an M. Sc

Francesco Napolitano

​Francesco is a Computer Scientist with a background in Mathematical Models and Machine Learning. He focused his M.Sc. (University of Salerno, SA, Italy) and Ph.D. (University of Salerno and the University of California, Irvine, CA, USA) on supervised and unsupervised data analysis, particularly for clustering of complex, high-dimensional data. He developed and applied data analysis techniques to problems from different fields, including fault detection in avionics and computational pharmacology, before focusing on bioinformatics and systems biology. Research Interests Francesco's main

Fuad Jamour

Fuad Jamour is a Ph.D. student in InfoCloud Research Group (CLOUD) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Panagiotis Kalnis. Research Interests Fuad's research interests include Data Management and Machine Learning. Education Profile ​B.Sc. of Computer Engineering, University of Jordan, Amman, Jordan.

Francesco Orabona

Associate Professor, Computer Science
Achieving "parameter-free" machine learning is the primary focus of Professor Francesco Orabona. He is particularly interested in designing superior algorithms to train deep learning models.

Fawaz Alsolami

Ph.D. Student, Computer Science
Research Interests Sequential optimization for inhibitory rules relative to length and coverage Analysis and design of classifiers based on inhibitory rules and trees Machine Learning and Data Mining Selected Publications F. Alsolami, T. Amin, M. Moshkov, B. Zielosko: Comparison of heuristics for optimization of association rules. CS&P 2015: 4-11 (2015) F. Alsolami, T. Amin, I. Chikalov, M. Moshkov, B. Zielosko: Dynamic programming approach for construction of association rule systems. CS&P 2015: 12-21 (2015) F. Alsolami, I. Chikalov, M. Moshkov: Comparison of heuristics for inhibitory rule

Eva Bones

Ph.D. Student, Computer Science
Eva Bones is a Ph.D. candidate in the Computer Science Program under the supervision of Professor Ivan Viola in the Nanovisualization Research Group at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Research Interest Eva is focusing on Computer Graphics and Visualization. Academic Advisors Ivan Viola, Professor, Computer Science.

Faisal Mushayt

M.S. Student, Computer Science
Education 2020.09 - Present M.S. in Computer Science, King Abdullah University of Science and Technology, KSA 2016.09 - 2020.08 B.S. in Computer Science, Boston University, USA Professional Profile 2018.06 - 2018.09 Data Scientist Intern, School of Business, LISH, Harvard, USA 2017.09 - 2018.05 Teaching Assistant, Math & Computer Science, Boston University, USA Areas of Expertise and Research Interests ​Machine Learning Learning in Physical Environments AI & Robotics Safety Engineering

Eslam Abdelrahman BAKR

Ph.D. Student, Computer Science
Eslam BAKR is a Computer Science Ph.D. candidate at the Visual Computing Center (VCC)/Vison-CAIR at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Mohamed Elhoseiny. Education and Early Career BS in Computer Engineering, Cairo University,(Sep 2012-Jul 2017)Studied programming, computer architecture, electronics, communications, waves, and computer networks. M.Sc in Computer Engineering, Cairo University,(Mar 2019-May 2022) Work Experience: TA at Zewail City of Science and Technology, (Sep 2020-21) Instructor at Faculty of Computer Science, Ain

Elnur Gasanov

Elnur Gasanov is a master student at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Richtárik in his research group. Education and Early Career Elnur Gasanov obtained his bachelor degree in Applied Mathematics and Physics from Moscow Institute of Physics and Technology (MIPT) in Moscow, Russia in 2018. Research Interest His research interests are in optimization and machine learning. Education Profile ​B.Sc., Applied Mathematics and Physics, Moscow Institute of Physics and Technology (State

Egor Shulgin

Ph.D. Student, Computer Science
Egor Shulgin is a CS student in the MS/Ph.D. program under the supervision of Professor Peter Richtarik at the Visual Computing Center (VCC) at the King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Education and Early Career Egor obtained his bachelor's degree in Applied Mathematics, Physics, and Computer Science from the Moscow Institute of Physics and Technology (MIPT) in 2019. He first came to KAUST for a research visit in 2019. Later, he joined Prof. Peter Richtarik's group as a MS/Ph.D. student. In 2021 Egor did a research internship with Distributed AI team at the

Emaad Ahmed Manzoor

Emaad Ahmed Manzoor obtained his M.S. degree in Computer Science under the supervision of Professor Panagiotis Kalnis at InfoCloud Research Group (CLOUD) in King Abdullah University of Science and Technology (KAUST) in 2016. Research Interests Emaad's research interests included Diffusion of information, influence in networks, Algorithms for machine learning and optimization on graphs. Education Profile MS Computer Science, King Abdullah University of Science and Technology (KAUST), Saudi Arabia (2013-2015). BE Computer Science, BITS - Pilani Goa Campus, India. (2008 - 2012) Professional

Dmitry Kovalev

Ph.D. Student, Computer Science
Education Dmitry obtained a B.S. degree from Moscow Institute of Physics and Technology, Moscow, Russia, in 2018. He is an M.S. in the Computer Science program at the King Abdullah University of Science and Technology as of Fall 2018. Publications Dmitry Kovalev, Samuel Horvath and Peter Richtárik. Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop, arXiv preprint arXiv:1901.08689, 2019. Dmitry Kovalev, Peter Richtárik, Eduard Gorbunov, Elnur Gasanov. Stochastic Spectral and Conjugate Descent Methods . Advances in Neural Information Processing

Dominik L. Michels

Associate Professor, Computer Science
Professor Michels is a leading computer scientist who proactively designs strategies to face upcoming digitization challenges. His outstanding research has been recognized by the scientific community within and beyond KAUST.

Di Wang

Assistant Professor, Computer Science
Professor Wang's research addresses issues and societal concerns arising from machine learning, particularly in the areas of privacy, security, safety, fairness, robustness, interpretability and transparency. He aims to develop provable and practical algorithms to address trustworthiness issues in machine learning.

Dimitrios Kleftogiannis

Dimitrios Kleftogiannis obtained his Ph.D. degree in Computer Science under the supervision of Professor Panagiotis Kalnis at InfoCloud Research Group in King Abdullah University of Science and Technology (KAUST), in 2016. Research Interests Dimitrios works mostly on knowledge discovery technologies with applications in Bioinformatics and Computational Biology. ​His research interests include Machine Learning, Artificial Intelligence and Data Analysis of NGS-derived data. He has worked extensively on the prediction of distal DNA regulatory elements focusing on the category of enhancers. He is

David Keyes

Senior Associate to the President, King Abdullah University of Science and Technology
David Keyes works at the interface of computational science and engineering and high performance computing, which he helped edit into the DNA of KAUST as a founding Dean in 2009 and then as director of the Extreme Computing Research Center. He currently serves as Senior Associate to the President, Chair of the KAUST RDIA Economies of the Future committee, and delegate to the Saudi Center for the Fourth Industrial Revolution.

Da Li

M.S. Student, Computer Science
Da Li is a Master student in Computer Science under the supervision of Professor Ivan Viola at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST).

Chisato Fujii

Chisato Fujii obtained her Master's degree in Computer Science under the supervision of Prof. Xin Gao at Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Research Interests Chisato's research interests included Bioinformatics and Computational Biology. Born in Japan, Chisato is a software engineer at a fashion technology startup in Cambridge. She works on the development of 3-dimensional garment rendering on a customizable body shape unique to the shopper's size. How would you describe your work/activities at KAUST? ''I studied a

Cheng-Long Wang

Ph.D. Student, Computer Science
Cheng-Long Wang is currently a third-year Ph.D. student at ‌Provable Responsible AI and Data Analytics (PRADA) Lab under the supervision of Professor Di Wang at King Abdullah University of Science and Technology (KAUST). His research is on machine unlearning, aiming to bridge the gap between personal/organizational data protection requirements and digital-world workflows.

Cheng Wang

Ph.D. Student, Computer Science
Cheng Wang received his PhD in the Computer Science Program under the supervision of Professor Helmut Pottmann at the Visual Computing Center (VCC). He is now an Assistant Professor at the University of East Anglia in the United Kingdom Education and Early Career Cheng received his Bachelor's Degree in Automation from Tsinghua University, China in 2016. He joined KAUST to continue his studies and received his Master's Degree in Computer Science in 2018. Research Interest Cheng is mainly interested in Computer Graphics, Geometric Computing, and Machine Learning. Education Profile M.S., Computer

Biao Zhang

Ph.D. Student, Computer Science
Biao Zhang is a Computer Science Ph.D. candidate at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Peter Wonka in his research group. Education and Early Career Biao Zhang obtained his bachelor degree in Mathematics and master degree in Applied Mathematics from Xi’an Jiaotong University in China. Research Interest Biao Zhang is mainly interested in machine learning and computer vision. Education Profile M.S., Applied Mathematics, Xi’an Jiaotong University, China B.S., Mathematics, Xi’an Jiaotong University

Bernard Ghanem

Professor Ghanem's research focuses on computer vision and machine learning, emphasizing large-scale video understanding, vision for automated navigation and theoretical foundations.

Azza Althagafi

Ph.D. Student, Computer Science
Azza Althagafi is a Ph.D. candidate in the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Robert Hoehndorf. Professional Profile ​2016 to present Graduate Student, KAUST, Thuwal, Saudi Arabia. 2015 - 2016: Teaching Assistant, Taif University, Taif, Saudi Arabia. 2014 – 2015: Volunteer Teaching Assistant, Umm Al-Qura University, Makkah, Saudi Arabia. 2013 – 2016: Assistant Trainer, Mawhiba, The King Abdulaziz and His Companions' Foundation for Giftedness and Creativity, Makkah, Saudi Arabia. Scientific and

Arturo Magana Mora

​Arturo Magana-Mora obtained his Ph.D. in Computer Science at the King Abdullah University of Science and Technology (KAUST) under the supervision of Prof. Vladimir Bajic in 2017 and later joined the National Institute of Advanced Industrial Science and Technology (AIST) in Japan as a postdoctoral research fellow in the Com. Bio Big-Data Open Innovation Lab (CBBD-OIL). Research Interest His research interests include the development of novel machine-learning and data mining techniques to address the complex problems in biology. His research work has resulted in several peer-reviewed

Azimkhon Ostonov

Ph.D. Student, Computer Science
Azimkhon Ostonov is a PhD candidate at King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Mikhail Moshkov in Extensions of Dynamic Programming, Machine Learning, Discrete Optimization (TREES). Education and Early Career Azimkhon Ostonov studied at the National University of Uzbekistan in Uzbekistan. He obtained his bachelor's degree in Applied Mathematics and Informatics in 2012 and his master's degree in Computer Systems and their Software in 2014. Azimkhon started his career as a junior programmer at Fido-Biznes in Tashkent. Then, he

Asaad S. Alghamdi

Arnaud Dethise

Ph.D. Student, Computer Science
Arnaud Dethise is a PhD candidate in Computer Science under the guidance of Professor Marco Canini. He completed his Bachelor's degree in Engineering and Master's degree in Computer Science in 2015 and 2017 respectively, from UCLouvain, Louvain-la-Neuve, Belgium. Arnaud has a strong interest in machine learning applied to distributed systems and is currently pursuing his doctoral research in this field. His specific research interests include Neural Networks, trustworthiness, verifiability and explainability, and model privacy, with a focus on exactness and provable guarantees. Arnaud’s goals

Anna Fruehstueck

Anna Fruehstueck was a Ph. D. candidate under the supervision of Prof. Peter Wonka at King Abdullah University of Science and Technology (KAUST) and successfully defended her PhD in Computer Science on April 17, 2023. Education and Early Career Anna Fruehstueck obtained a Bachelor’s Degree in Media Informatics and Visual Computing from Vienna University of Technology in Austria in 2011. Later on in 2015, she received her M.Sc. degree in Visual Computing from Vienna University of Technology in Austria. During her studies, Anna worked as software developer at SimVis in Vienna, Austria. And in

Alyazeed Basyoni

M.S. Student, Computer Science
Alyazeed Basyoni is a CS Master Student under the supervision of Professor Peter Richtarik at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career Alyazeed joined Carnegie Mellon University in Pittsburgh, Pennsylvania and earned his bachelor degree in Computer Science with minor in Mathematics in 2018. Education Profile B.S., Computer Science, Carnegie Mellon University, USA, 2018

Alya AlShaikh

Alya AlShaikh is currently an undergraduate student at Alfaisal University in Riyadh, KSA. She is pursuing a Software Engineering BSc specializing in Cyber Security and Machine Learning. Alya joined the Secure Next Generation Resilient Systems Lab (SENTRY) led by Professor Konstantinou as an Accelerated Bachelor’s and Master's (ABM) student. Her research interests include applying machine learning techniques to enhance cyber defense strategies.

Alexandra Irger

M.S. Student, Computer Science
Alexandra Irger is a Master student in Computer Science under the supervision of Professor Ivan Viola at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career Before coming to KAUST, Alexandra graduated from TU Wien with a BSc in Media Informatics and Visual Computing in 2023. Research Interest Alexandra is focusing on Scientific Visualization and Computer Graphics.

Ahmed Abdulmajeed Alabdulkarim

Ahmed Abdulmajeed Alabdulkarim earned a master's degree from KAUST in 2011. His research interests are in the field of big data, artificial intelligence and machine learning, and he is now pursuing his Ph.D. at the Massachusetts Institute of Technology (MIT) in the U.S. He received his bachelor's degree with honors in computer science from King Saud University, where he graduated at the top of his class. From a young age, Alabdulkarim was passionate about computers. Studying computer science was his drive and motivation to continue his university studies. He explained that he chose KAUST for

Ahmed Mohamed Abdelmoniem Sayed

Dr. Ahmed Sayed is a Postdoctoral research fellow at ECRC, working with Professor Marco Canini. He is focused on tackling challenging problems related mainly to improve the performance and understanding of machine learning systems and their usage for optimizing and automating computer and network systems. Why KAUST? KAUST is the beacon for innovation and creativity in the middle-east area and they follow the model of HKUST to become a world-wide top-tier leading university in science and technology. Why networking The intersection point between Networked Systems & Machine Learning is a very

Abdulwahab Felemban

Ph.D. Student, Electrical and Computer Engineering CEMSE, King Abdullah University of Science and Technology (KAUST),completed MSc under the supervision of Prof. Tareq Al-Naffouri.

Adil Salim

Postdoctoral Research Fellow, Computer Science
Adil Salim is a Postdoctoral Research Fellow working with Professor Peter Richtarik at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Dr. Salim earned his Master degrees in Data Science and Statistical Engineering from ENSAE ParisTech and in Probability Theory and Stochastic Modeling from Paris XI University in France in 2015. He received his Ph.D. in Machine Learning from Télécom ParisTech in France in 2018. Before joining KAUST, Adil worked in many positions starting from an Auditor for banks and insurance companies

Abeer Almutari

M.S. Student, Computer Science
Abeer Almutari obtained her Master's degree in Computer Science under the supervision of Professor Robert Hoehndorf at Bio-Ontology Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Abeer's research interests included bioinformatics, machine learning and building new automated models using artificial intelligence. Education ​B.Sc. Electrical & Computer Engineering, King Abdul-Aziz University, Jeddah, Saudi Arabia, 2016

Abdulellah Abualshour

Abdulellah Abualshour is a Ph.D. student under the supervision of Prof. Bernard Ghanem at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career Abdulellah was chosen to join the KAUST Gifted Student Program (KGSP). He is a graduate of Rutgers University where he received a B.S. degree in Computer Science, with a focus on data science and artificial intelligence. Regarding his early career, Abdulellah did many internships during his studies. In 2015, he worked for 1 year as a Public Relations Officer at Rutgers U niversity in

Abdullah Khamis

Abdullah Khamis graduated with a Ph.D. degree in Computer Science from KAUST under the supervision of Professor Vladimir Bajic and Associate Professor Xin Gao. He now works as the Director of Data Science in the Center for Strategic Studies (CSS) of the Royal Court of Saudi Arabia. He leads the Data Science team in developing several machine learning based predictive models such as sentiment analysis and prediction. Research Interest Abdullah's research interests were in bioinformatics and machine learning.

Abdullah Muhammad Kamal

Visiting Student, Computer Science
Abdullah Muhammad-Kamal is a visiting student from the University of Warwick, United Kingdom at (BORG) Bio-Ontology Research Group of Professor Robert Hoehndorf at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Currently on his placement year, interning with the Bio-Ontology Research Group at KAUST, he will be graduating with his Bachelor's degree in Computer and Management Sciences in the summer of 2021. Research Interests ​Abdullah's research interests include Bioinformatics, Machine Learning, and Data Science.
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Yasmeen Alsaedy

M.S. Student, Computer Science
She is an AI researcher specializing in generative AI and deep learning. With a strong academic foundation and hands-on experience in machine learning, data science and artificial intelligence, she is passionate about advancing AI applications in real-world scenarios. She has contributed to cutting-edge projects, published research, and collaborated on innovative solutions that bridge the gap between AI theory and practice. Her expertise includes designing and implementing advanced data models, AI-driven visual/text computing, and deep learning architectures.

Yasser Jaber

Visiting Student Researcher ,Machine Learning Engineer at KAUST (King Abdullah University of Science and Technology).
Syed Abidullah Fareed

Syed Abidullah Fareed

Visiting Student (former), Information Science Lab

Ahmed Youssef Ragab Radwan

Visiting Student (former), Information Science Lab
Visiting student at the Information Science Lab, in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST)

AI4GH Seminar Series | Predicting Protein Function and Phenotype Associations

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B2 R5220

The amount of available protein sequences is rapidly increasing, mainly as a consequence of the development and application of high ​throughput sequencing technologies in the life sciences.

Contributions to Computational Methods for Association Extraction from Biomedical Data: Applications to Text Mining and In Silico Toxicology

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B2 L5 R5220

Abstract The task of association extraction involves identifying links between different entities. Here, we make contributions to two applications related to the biomedical field. The first application is in the domain of text mining aiming at extracting associations between methylated genes and diseases from biomedical literature. Gathering such associations can benefit disease diagnosis and treatment decisions. We developed the DDMGD database to provide a comprehensive repository of information related to genes methylated in diseases, gene expression, and disease progression. Using DEMGD, a

Causality-based new drug development: Some successful cases and a new challenge

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B2 L5 R5209

Abstract The success rate of new drug development is extremely low. It is even lower when the new drug has a novel mechanism of action. From my experience, I propose that the success rate can be dramatically increased by predicting the effects of a drug in humans based on the causality-confirmed data. It is dangerous to develop new drugs based on the data in which causality is not confirmed. In biology, there are three different types of relationships in which the causality is confirmed, i.e. the relationships between parent and child, between gene and phenotype and between intervention and

VSIM: Visualization and Simulation of Genomes for Premarital Testing

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B3 L5 R5209

Abstract Interpretation and simulation of the large-scale genomics data are very challenging, and currently, many web tools have been developed to analyze genomic variation which supports automated visualization of a variety of high throughput genomics data. We have developed VSIM an automated and easy to use web application for interpretation and visualization of a variety of genomics data, it identifies the candidate disease variants by referencing to four databases Clinvar, GWAS, DIDA, and PharmGKB, and predicted the pathogenic variants. Moreover, it investigates the attitude towards

AI4GH Seminar Series - VSIM: Visualization and Simulation of Genomes for Premarital Testing

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B2 R5220

Interpretation and simulation of the large-scale genomics data are very challenging, and currently, many web tools have been developed to analyze genomic variation which supports automated visualization of a variety of high throughput genomics data.

Knowledge-based methods in biomedicine

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B9 L2 H1

The life sciences have invested significant resources in the development and application of semantic technologies to make research data accessible and interlinked, and to enable the integration and analysis of data. Utilizing the semantics associated with research data in data analysis approaches is often challenging. Now, novel methods are becoming available that combine symbolic methods and statistical methods in Artificial Intelligence. In my talk, I will describe how to apply knowledge-based methods for the analysis of biological and biomedical data, in particular identification of gene-disease associations and drug targets.

Reproducibility in Computer Science - Part 1

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Reproducibility in Computer Science - Part 2

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Reproducibility in Computer Science - Part 3

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

KAUST Research Conference Digital Health 2020

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B19 L2 H1

Computational Bioscience Research Center at King Abdullah University of Science and Technology is pleased to announce the KAUST Research Conference on Digital Health 2020.

Amazon Web Services (AWS) ML Immersion Day

Mohib Khan, Hesham Abouelmagd, Shijaz Abdulla (AWS)

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B19 H1

The ML Hub with the support of the AI Initiative, is excited to be hosting the AWS ML Immersion Day! Join us for a full-day immersion tutorial and hands-on lab on Amazon’s ML tools. The program includes an introduction to AWS AI and machine learning services and hands-on module on Amazon Lex and SageMaker.

Machine Learning Scan of Complete Genome Sequences of 4,500 Rice Varieties reveals Secrets in the Domestication Process of Crops

Takashi Gojobori, Distinguished Professor, Bioscience

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B9 L2 H2 R2325

In the history of humankind, domestication was invented as anthropogenic evolution that fulfills mankind’s critical food demand. The domestication is simply based on mating processes and subsequent selection processes to pick up better hybrid offspring that have advantageous combinations of genomes. Taking advantage of machine learning classifier, we discovered a number of sub-genomic regions that have been incorporated in the rice genomes through production of hybrid offspring during domestication. This so-called “introgression” event is disclosed as an essential key of domestication process. This eventually leads to construction of the AI-aided Smart Breeding Platform to accumulate all the breeding histories of crop species into an Integrated Breeding Knowledgebase.

Artificial Intelligence methods for precision health: Applications to rare disease and cancer research

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B9 L2 H2

Abstract The life sciences have invested significant resources in the development and application of semantic technologies to make research data accessible and interlinked, and to enable the integration and analysis of data. Utilizing the semantics associated with research data in data analysis approaches is often challenging. Now, novel methods are becoming available that combine symbolic methods and statistical methods in Artificial Intelligence. In my talk, I will show how to incorporate biological background knowledge in machine learning models for the identification of gene-disease

Rising Stars in AI Symposium 2022 at KAUST

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B4 B5 A0215

Rising Stars in AI Symposium 2022 at KAUST The "Rising Stars in AI Symposium" is geared towards young researchers (including PhD students, PostDocs, young faculty) who have recently published promising work at leading venues. There will be dozens of brief in-person presentations about papers recently accepted at major AI conferences such as NeurIPS, CVPR, EMNLP, ACL, ICML, ICLR, etc. All speakers will be encouraged to start with an intro for non-AI experts. To view the complete program and for more event details, please visit the symposium website. Attend the Symposium The symposium will be

Designing High-Performance and Scalable Middleware for HPC, AI, and Data Science

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B9 L2 R2322 H1

This talk will focus on challenges and opportunities in designing middleware for HPC, AI (Deep/Machine Learning), and Data Science. We will start with the challenges in designing runtime environments for MPI+X programming models by considering support for multi-core systems, high-performance networks (InfiniBand and RoCE), GPUs, and emerging BlueField-2 DPUs. Features and sample performance numbers of using the MVAPICH2 libraries will be presented. For the Deep/Machine Learning domain, we will focus on MPI-driven solutions to extract performance and scalability for popular Deep Learning frameworks (TensorFlow and PyTorch), large out-of-core models, and Bluefield-2 DPUs.

Applied AI for sustainable development

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B9 L2 R2322 H1

Skoltech Applied AI center’s mission is to create AI models and frameworks for solving the problems of sustainable development of industry and economy. In my presentation, I will overview the current center's activities, applied and fundamental problem statements, and corresponding recent results.

Bridging the Gap between the Academia and Industry by Extending High-Quality Research to Practical Business Needs

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B9 L2 R2322 H1

This seminar focuses on providing the audience with the context and scope of our internship program. The program is for the young and talented graduate students with an active interest in solving real-world problems. Some of the projects that will be presented in the seminar are actively developed in Elm and include domains such as computer vision, robotics and automation, healthcare, IoT, video analytics, and NLP. The seminar will serve as a launch pad to allow students to discuss their future interests and aspirations with the speaker. It will also enable them to develop a better awareness of domains more relevant to their future research aspirations.

Machine-learning-assisted Quality of Transmission Estimation in Optical Networks

Andrea Bianco, Full Professor, Electronics and Telecommunications Department at Politecnico di Torino

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B9 L4 R4125

Machine Learning (ML) tools have recently been adopted for a wide range of automated operations in optical networking, moving fundamental steps towards the paradigm of zero-touch infrastructures. One example of such tasks is estimating the Quality of transmission of a lightpath prior to its establishment, which is particularly challenging due to the non-linear characteristics of signal propagation in optical fibers and to the often-incomplete knowledge of equipment parameters. This talk provides an overview of the contribution of my research team in the field of ML-based lightpath QoT estimation, including transfer learning approaches for inter-domain model adaptation, active learning for model building with small-sized training dataset, quantification of prediction uncertainty, and adoption of Explainable AI framework to expose the internal decisional mechanisms of trained models.

Creating Cognitive Enterprises Through Decision Intelligence

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B9 L2 H2 H2

It is difficult to predict the future, but ultimately, what matters is creating the future we want to live in. Our past and present are the product of our previous decisions. The decisions we make today will pave the way for the future. It is important that the right decision is made at the right time leading to the right outcome, paving the way towards the desired future.

The Prospect of Generative Language Processing

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B9 L2 H2 H2

In the evolving landscape of artificial intelligence, generative models are revolutionizing our interface with computational systems and reshaping societal paradigms. For example, foundation models have the potential to transform content creation across languages, offering discovery and productivity pathways for humans to engage with one another and their environment. This talk sketches the core methodologies propelling this groundbreaking progress, charting a grand vision for generative natural language processing.

Resistance is Futile: Balancing Opportunities and Security Challenges of Federated Learning

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B4 L5 R5220

The rapid growth of Artificial Intelligence (AI) and Deep Learning mirrors an infectious phenomenon. While AI systems promise diverse applications and benefits, they bear substantial security and privacy risks. Indeed, AI represents a goldmine for the security and privacy research domain.

Data, AI and Cybersecurity: Synergies and Interconnection

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B9 L2 H2 H2

This Dean's Distinguished Lecture is part of the ECE Graduate Seminar. Modern Artificial Intelligence (AI) technologies, led by deep learning, have gained unprecedented momentum over the past decade.

Averaging, Momentum, and Schedulers in Optimization for Deep Learning

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B9 L3 R3128

In this talk, I will present some work in progress on practical optimization methods for deep learning. We will start with a discussion of several empirical techniques that enable training of large-scale models in language and vision tasks, including weight decay, averaging, and schedulers. We will then look at a new approach that we call schedule-free due to its ability to work without a pre-defined time horizon. I will share some details about the theory for these methods, explain why they might be useful in practice and then shed some light on their limitations. This talk will be oriented towards people who already have some knowledge of optimization methods.

Computer vision: Teaching computers how to see the world

1 min read ·
Ghanem works in the Visual Computing Center (VCC) and is interested in computer vision, image processing, and machine learning.

Amin Allam (CEMSE PhD) wins the second prize in the DREAM Challenge 1B

1 min read ·
Amin Allam (CEMSE PhD student in the InfoCloud group) supervised by Prof Panos Kalnis has won the second prize in the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge 1B. Amin Allam utilized Bayesian inference to achieve the 2nd place.

Professor Robert Hoehndorf's PhenomeNET method wins at Ontology matching workshop

1 min read ·
Professor Robert Hoehndorf and Postdoctoral Fellow Miguel Angel Rodriguez Garcia are the winners of the Ontology Alignment Evaluation Initiative Challenge sponsored by Pistoia Alliance.

Professors discuss big data and human health in latest Sci-Café

1 min read ·
​Professors Takashi Gojobori, Xin Gao and Robert Hoehndorf spoke about big data and human health at the Sci-Café event held on February 27. Their talk, which was moderated by James Calvin, Vice President for Academic Affairs, aimed to answer the question, "Can Big Data Solve My Health Problems?" This event was open to all members of the KAUST community.

Cocoon finally out: Professor Marco Canini together with VMware, Samsung, and Microsoft craft the ultimate SDN ally

1 min read ·
With the recent emergence of software-defined networking, which brings programmability and lowers the barrier for new functionalities into networks, the academic and industry communities have become very interested in the problem of network verification.

The awesome scope of big data

1 min read ·
Sifting through huge amounts of data may bring a better understanding of whale shark social structures, protein targets for drug therapies and disease-causing genes.

Sarah Alghamdi defends her master's thesis

1 min read ·
Sarah Alghamdi successfully defended her master's thesis on April 10, 2018. Her project, entitled "Ontology Design Patterns for Combining Pathology and Anatomy: Application to Study Ageing and Longevity in Inbred Mouse Strains", was well-received by a panel of professors that included Professor Vladimir Bajic, Associate Professor Xin Gao and Assistant Professor Robert Hoehndorf.

Maxat Kulmanov defends his thesis proposal

1 min read ·
Maxat Kulmanov, a Ph.D. candidate under the supervision of Assistant Professor Robert Hoehndorf, defended his thesis proposal on July 31, 2018.

Alumni Focus: Ahmed Abdulmajeed Alabdulkarim, M.S. '11

1 min read ·
Ahmed Abdulmajeed Alabdulkarim earned a master's degree from KAUST in 2011. His research interests are in the field of big data, artificial intelligence and machine learning, and he is now pursuing his Ph.D. at the Massachusetts Institute of Technology (MIT) in the U.S. He received his bachelor's degree with honors in computer science from King Saud University, where he graduated at the top of his class. From a young age, Alabdulkarim was passionate about computers. Studying computer science was his drive and motivation to continue his university studies. He explained that he chose KAUST for

KAUST Associate Professor Xiangliang Zhang talks about artificial intelligence

1 min read ·
Xiangliang Zhang, associate professor of computer science in the University's Computer, Electrical and Mathematical Science and Engineering division, recently gave an invited Early Career Spotlight Talk at the 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence (IJCAI-ECAI-18) 2018. During the event—which was held in Stockholm, Sweden, from July 13 to 19—Zhang and 21 international peers presented on all areas of artificial intelligence (AI).

Azza Althagafi defends her Master's thesis

1 min read ·
Azza Althagafi, a student under the supervision of Assistant Professor Robert Hoehndorf, defended her Master's thesis on November 1, 2018.

Tweaking tools to track tweets over time

1 min read ·
Computer model learns to identify Twitter users’ evolving interests by analyzing their Tweets.

KAUST Researchers Are the First to Generate Images of Realistic and Highly Detailed Texture Maps of Gigapixel in Size Using Deep Neural Networks

1 min read ·
KAUST researchers Anna Fruehstueck, Dr. Ibraheem Alhashim, and Prof. Peter Wonka have developed a novel technique to generate images of realistic and highly detailed texture maps using deep neural networks. The texture images synthesized by their system TileGAN can be of gigapixel size and are created by seamlessly merging smaller texture blocks into a single large image. The underlying neural networks are trained using high-resolution images such as detailed satellite imagery, maps and famous paintings.

AI learns complex gene-disease patterns

1 min read ·
A deep learning model improves the ability to identify genes potentially involved in disease.

Database to support infectious disease research

1 min read ·
Linking disease pathogens to clinical signs and symptoms through a database could support research into the molecular mechanisms of infectious diseases.

Symbol of change for AI development

1 min read ·
Bridging the knowledge gap in artificial intelligence requires an embedding function that helps step between different types of "thinking."

CEMSE New Faculty: Mohamed Elhoseiny, Assistant Professor of Computer Science

1 min read ·
Prior to joining the KAUST CEMSE Division earlier this year, Mohamed Elhoseiny received his Ph.D. degree from Rutgers University, New Brunswick in 2016, before spending over two years working as a postdoctoral researcher at Facebook in the company’s AI research wing. Elhoseiny joins the Division as an assistant professor of computer science based in the KAUST Visual Computing Center (VCC). He will also act as the PI of the KAUST Computer Vision, Content AI (Vision-CAIR) Research Group. Outside of his duties at KAUST, he is also acting as an artificial intelligence (AI) research consultant for Baidu Research, Silicon Valley AI Lab.

AI reveals nature of RNA-protein interactions

1 min read ·
A deep learning tool could help in structure-based drug discovery.

ActivityNet is featured in the 2019 Artificial Intelligence Index

1 min read ·
ActivityNet is featured in the AI Index as the benchmark for the algorithms that can recognize human actions and activities from videos.

LD Motif Finder locates ancient hidden protein patterns

1 min read ·
A machine learning method has identified highly elusive amino acid sequences involved in cell morphogenesis and adhesion and in diseases like cancer.

AI uses your imagination

1 min read ·
The psychology of human creativity helps artificial intelligence imagine the unseen.

Approximating a kernel of truth

1 min read ·
Machine learning tasks using very large data sets can be sped up significantly by estimating the kernel function that best describes the data.

CEMSE Division startups in focus: Peregrine Genomics

1 min read ·
The future has already arrived when it comes to the most exciting and promising field of modern medicine—precision medicine.

Flattening the sentimental curve

1 min read ·
-By Meres J. Weche, KAUST News As municipal, regional and national governments have mandated longer and more restrictive isolation time periods to slow down the spread of the COVID-19 pandemic and "flatten the curve," KAUST Associate Professor of Computer Science Xiangliang Zhang works on flattening the "emotional curve." Zhang's research focuses on developing reliable algorithms to analyze complex data sets through machine learning and artificial intelligence. Her research group specifically uses computer models to analyze social media posts on Twitter to study people's interests. When the

Peeling back the layers of deep machine learning

1 min read ·
A layer-based approach raises the efficiency of training artificial intelligence models.

KAUST CS student recognized with AAAI Outstanding Program Committee Award

1 min read ·
Konstantin Mishchenko, a Ph.D. student under the supervision of Professor Peter Richtárik, has been selected as one of the Association for the Advancement of Artificial Intelligence’s (AAAI) 12 Outstanding Program Committee Members for 2020. Mishchenko was presented with a certificate in recognition of his outstanding service at AAAI-20 held in New York from February 7-12.

Tracking the Curve: Analyzing the Emotional Response to COVID-19

1 min read ·
Hashtags like #covid19 and #coronavirus help us stay up to date on the developments of the new coronavirus pandemic. But beyond breaking news, tweets also offer a glimpse into the emotional side of the COVID-19 crisis.

AI that's built to save lives

1 min read ·
Associate Professor Xin Gao and his group have developed an artificial-intelligence (AI) based solution to help increase COVID-19 testing accuracy. Identifying cases of early stage infection has been particularly challenging for frontline clinicians. Gao's AI-based model, which aims to increase accuracy, has been put to immediate use at King Faisal Specialist Hospital (KFSH) in Riyadh.

KAUST Prospective Student: Siyuan Chen

1 min read ·
Siyuan Chen is a 22-year-old graduate who comes to KAUST from the School of Communication and Information Engineering, UESTC, China. Chen will join the University in the fall of 2020 as an M.S./Ph.D. candidate in the KAUST Structural and Functional Bioinformatics research group under the supervision of Professor Xin Gao.

Robert Hoehndorf promoted to associate professor at KAUST

1 min read ·
KAUST faculty member Robert Hoehndorf was recently promoted from the rank of assistant professor to associate professor. Hoehndorf’s promotion caps a year-long process where the German researcher’s scientific and scholarly output was measured and evaluated by internal and external reviews.

How AI helps improve COVID-19 testing

1 min read ·
Following a call by President Tony Chan for KAUST PIs to contribute through their research capabilities to alleviate the COVID-19 pandemic, efforts coordinated by Donal Bradley, KAUST vice president for research, and Pierre Magistretti, KAUST dean of the Biological and Environmental Science and Engineering division, mobilized a group of faculty to form the Rapid Research Response Team (R3T).

Former KAUST VSRP student wins Best Talk Award at ISMB 2020

1 min read ·
Runar Reve, a former KAUST Visiting Student Research Program (VSRP) student, recently received the International Society for Computational Biology’s (ISCB) Bio-Ontologies COSI – Best Talk Award at the 28th Conference on Intelligent Systems for Molecular Biology (ISMB) 2020.

Meet KAUST prospective student: Zhenwen Liang

1 min read ·
Zhenwen Liang, 22, is a computer science graduate from the University of Electronic Science and Technology of China (UESTC), China. Zhenwen will join KAUST in the fall of 2020 as a M.S./Ph.D. candidate and member of the Machine Intelligence and kNowledge Engineering research group under the supervision of Professor Xiangliang Zhang.

KAUST-Stanford neural network paper accepted for presentation at ECCV 2020

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A novel KAUST-Stanford University research paper by Panos Achlioptas (Stanford), Ahmed Abdelreheem (KAUST), Fei Xia (Stanford), Mohamed Elhoseiny (KAUST) and Leonidas Guibas (Stanford) was accepted for presentation at the 16th European Conference on Computer Vision (ECCV 2020).

Meet KAUST prospective student: Xiaopeng Xu

1 min read ·
Xiaopeng Xu is a computer science graduate who will join KAUST in the fall of 2020. Xu obtained a master’s degree in computer science from KAUST, and a bachelor’s degree in bioinformatics from Huazhong University of Science and Technology, respectively. He will join KAUST in the fall of 2020 as a Ph.D. candidate and a member of the KAUST Structural and Functional Bioinformatics group under the supervision of Professor Xin Gao.

Meet KAUST student: Konstantin Burlachenko

1 min read ·
Konstantin Burlachenko holds a master’s degree in computer science from Bauman Moscow State Technical University (BMSTU), Russia. After graduating from BMSTU, he worked in several prominent Moscow companies, such as Huawei, NVIDIA, and Yandex. He joined KAUST in August 2020 as a Ph.D. candidate and member of Professor Peter Richtarik's Optimization and Machine Learning Lab.

Meet KAUST prospective student: Feng Liang

1 min read ·
Feng Liang is a computer science and engineering graduate who will join KAUST from the Southern University of Science and Technology (SUSTech), China. Feng will join the University this fall as a M.S. candidate and member of the KAUST Nanovisualization Research Group under the supervision of Professor Ivan Viola.

Machine learning at speed

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Optimizing network communication accelerates training in large-scale machine-learning models.

SDAIA, KAUST sign MoU to develop AI research and innovation in Saudi Arabia

1 min read ·
Saudi Gazette report JEDDAH — The Saudi Data and Artificial Intelligence Authority (SDAIA) and King Abdullah University of Science and Technology (KAUST) have signed a memorandum of understanding to unite efforts to develop Artificial Intelligence (AI) research and modern technologies in the Kingdom. The MoU was signed by the CEO of the National Center for Artificial Intelligence, the innovative arm of SDAIA, Dr. Majid Al-Tuwaijri, and by KAUST President, Professor Tony Chan, the Saudi Press Agency reported on Thursday. The MoU supports science and technology research and improves its quality

Drop the zeros for a performance boost

1 min read ·
An eight-fold speed up of deep machine learning can be achieved by skipping the transmission of zero values.

Computer learns to write captions with feeling

1 min read ·
Mohamed Elhoseiny and his PhD student Youssef Mohamed are teaching AI to capture the full spectrum of human emotions when annotating artwork in order to reduce emotional bias in computer-generated captions.

Safeguarding the right to be forgotten

1 min read ·
An open-source software can help align artificial intelligence applications in healthcare with data privacy regulations.

AI tool predicts function of unknown proteins

1 min read ·
Powered by logic and large language models, DeepGO-SE predicts the biological role of proteins and could be used to aid drug discovery.

Zhenwen Liang

Ph.D. Student, Computer Science
Zhenwen Liang is a M.S./Ph.D. candidate in the KAUST Machine Intelligence and kNowledge Engineering research group under the supervision of Professor Xiangliang Zhang. Before joining KAUST, Zhenwen obtained a bachelor's degree in computer science from the University of Electronic Science and Technology of China (UESTC), China. Research Interests Zhenwen's research interests include AI research and machine learning.

Youssef S. Mohamed

Ph.D. Student, Computer Science
Education Youssef Mohamed is a Computer Science Ph.D. candidate at the Visual Computing Center (VCC)/Vison-CAIR at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Mohamed Elhoseiny Bachelor's Degree in Communication and Information Engineering, University of Science and Technology in Zewail City, Cairo, Egypt (2013-2018) Master's Degree in Computer Science, University of Tartu, Tartu, Estonia (2019-2022) Research Interests I am interested in developing AI that is aware of the Emotional and Cultural aspects of human intelligence. We study human

Shahad Qathan

M.S. Student, Computer Science
Shahad Qathan is an MS candidate in the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Robert Hoehndorf. Research Interests Artificial Intelligence, more specifically Machine Learning and its applications in healthcare and Bioinformatics Education Profile B.Sc, Computer Science, King Abdulaziz University, Jeddah, Saudi Arabia, 2020

Sarah Alghamdi

Ph.D. Student, Computer Science
Sarah Alghamdi is a Ph.D. candidate in the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Robert Hoehndorf. Research Interests Sarah's research interests include applications of artificial intelligence and using statistical methods to genomics and healthcare. Professional Profile Program Committee for the International Conference of Biomedical Ontology (ICBO 2022) Program Committee for the International Society of Molecular Biology (ISMB 2022) - Bio-Ontology track 2018-2021: Teaching Assistant positions: CS

Sakhaa Alsaedi

Ph.D. Student, Computer Science
​Sakhaa Al-Saedi completed her bachelor's degree in Computer Science in 2016 at Taibah University. She is the founder of the Medvation startup company, inventing educational kits that teach children concepts of robotics and Machine Learning (ML) in fun ways. She worked as a product developer at the Namma Al-Munawara company, Madinah, before starting her master's degree at KAUST in 2017. She is currently working with the Bio-Ontology Research Group (BORG) of Professor Robert Hoehndorf on human genome sequencing to evaluate the impact of Saudi-specific allele frequencies on variant calling

Runar Reve

Visiting Student, Bio-Ontology Research Group
​Runar Reve is from Klepp, Rogaland, in Norway. He is currently in his industry year as a visiting student for his bachelor's degree in Artificial Intelligence and Robotics at Aberystwyth University in Wales and will obtain his degree by the summer of 2021. He is currently working with Professor Robert Hoehndorf Bio-Ontology Research Group (BORG) on genome-wide association study (GWAS) to find variants, or sets of variants, in genomes that can result in genetic disease.​ Research Interests Genome-Wide Association Studies​. Education ​Undergrad in Artificial Intelligence (with integrated year

Robert Hoehndorf

Associate Professor, Computer Science
Robert Hoehndorf is an Associate Professor of Computer Science at KAUST. His research focuses on bioinformatics, knowledge representation and reasoning, Semantic Web technologies, and neuro-symbolic methods.

Othman Soufan

Research Interests I’m a Ph.D. in Computer Science; currently focusing on developing drug discovery and biomedical applications through data mining and graph mining techniques. I excel in analyzing data, extracting unseen useful patterns and achieving a better informative decision-making process. The result of my work can be found in one filed patent and several peer-reviewed publications in international journals ranked top in the respective domain of specialty. I have a strong business background, both academic and empirical, derived from my education and family business. Being transparent
KAUST-CEMSE-CS-Vision-CAIR-Mohamed-Elhoseiny

Mohamed Elhoseiny

Associate Professor, Computer Science
Professor Elhoseiny’s research focuses on developing affective artificial intelligence that understands and generates novel visual content. He has contributed to and led numerous seminal works of affective AI art creation.

Maxat Kulmanov

Research Scientist, Bio-Ontology Research Group
Maxat Kulmanov is a Research Scientist in the Bio-Ontology Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Maxat's research interests include bioinformatics, knowledge representation and reasoning, machine learning, neural networks, semantic web, and algorithms. He is interested in knowledge discovery and data integration using artificial intelligence and semantic web technologies in biology and biomedicine. Professional Profile 2020-Present: Postdoctoral Research Fellow, Computational Bioscience Research Center, King Abdullah University of

Marco Canini

Associate Professor, Computer Science
Professor Canini’s research seeks to improve networked-system design, implementation and operation concerning vital properties such as reliability, performance, security and energy efficiency.

MD Nurul Muttakin

Ph.D. Student, Computer Science
Nurul Muttakin, is an M.S./Ph.D. candidate in the Bio-Ontology Research Group (BORG) under the supervision of Professor Robert Hoehndorf. Before joining KAUST Nurul completed his B.Sc. in CSE from Bangladesh University of Engineering and Technology, Bangladesh. Research Interest Nurul is Interested in the application of Artificial Intelligence in Bioinformatics tasks like protein function prediction as well as understanding the theory behind it. Education Profile B.Sc. in CSE, Bangladesh University of Engineering and Technology, Bangladesh

Kexin Niu

Ph.D. Student, Bioengineering
Kexin Niu, is an M.S./Ph.D. candidate in the Bio-Ontology Research Group (BORG) under the supervision of Professor Robert Hoehndorf. Before joining KAUST Kexin obtained her bachelor's degree in Bioinformatics from Southern University of Science and Technology, Shenzhen, China. "What I like best about KAUST is the communication of diverse cultures and diverse disciplines, which enables me to have more possibilities." Research Interest Kexin’ s Research interests include but are not limited to bioinformatics and application of machine learning method in biomedical data. Education Profile 2016 -

Jürgen Schmidhuber

Professor, Computer Science
Professor Schmidhuber is a renowned computer scientist, best known for his pioneering work in artificial intelligence (AI), deep learning and artificial neural networks. He has authored more than 350 peer-reviewed papers and has provided AI strategies to several governments.

Jesper Tegner

Professor, Bioscience
Jasper Tegner is an M.D./Ph.D., innovator and professor with over 350 publications, dedicated to exploring the interconnected mechanisms underlying life, intelligence and the mind. His research aims to advance fundamental progress in artificial intelligence by moving beyond engineering to understand the intrinsic modes of operation within cells, between cells and within the brain.

Haozhe Liu

Ph.D. Student, Computer Science
Haozhe Liu is a PhD student at AI Initiative, King Abdullah University of Science and Technology (KAUST) under the supervision of Prof.Jürgen Schmidhuber.

Feng Liang

Feng Liang is a former member of the Nanovisualization Research Group at KAUST lead by Professor Ivan Viola. Before joining KAUST, Feng obtained a bachelor's degree in computer science and engineering from the Southern University of Science and Technology (SUSTech), China. Research Interests Feng's research interests include artificial intelligence and computer graphics.

Eslam Abdelrahman BAKR

Ph.D. Student, Computer Science
Eslam BAKR is a Computer Science Ph.D. candidate at the Visual Computing Center (VCC)/Vison-CAIR at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Mohamed Elhoseiny. Education and Early Career BS in Computer Engineering, Cairo University,(Sep 2012-Jul 2017)Studied programming, computer architecture, electronics, communications, waves, and computer networks. M.Sc in Computer Engineering, Cairo University,(Mar 2019-May 2022) Work Experience: TA at Zewail City of Science and Technology, (Sep 2020-21) Instructor at Faculty of Computer Science, Ain

Dimitrios Kleftogiannis

Dimitrios Kleftogiannis obtained his Ph.D. degree in Computer Science under the supervision of Professor Panagiotis Kalnis at InfoCloud Research Group in King Abdullah University of Science and Technology (KAUST), in 2016. Research Interests Dimitrios works mostly on knowledge discovery technologies with applications in Bioinformatics and Computational Biology. ​His research interests include Machine Learning, Artificial Intelligence and Data Analysis of NGS-derived data. He has worked extensively on the prediction of distal DNA regulatory elements focusing on the category of enhancers. He is

Deng Luo

Ph.D. Student, Computer Science
Deng Luo is a Ph.D. Candidate in the Computer Science program under the supervision of Professor Ivan Viola at the Visual Computer Center (VCC) at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia. Education and Early Career Deng Luo earned his Bachelor's degree in Bioinformatics from Southern University of Science and Technology in Shenzhen, China in 2016. He has a multidisciplinary background, including bioinformatics, molecular biology, computer science, and entrepreneurship. He joined KAUST and received his Master's degree in Bioscience in 2019. Deng was trained as

Chi-Han Peng

Research Scientist, Computer Science
Chi-Han Peng was a Research Scientist working with Professor Peter Wonka at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). He is an Assistant Professor at the School of Information Science and Technology (SIST) at ShanghaiTech University. Education and Early Career Before joining KAUST, Dr. Peng was a postdoctoral research associate at University College London (UCL), working with Prof. Niloy Mitra. He worked at Nogle, a private company based in Taipei, on the 3D modeling and simulation of real-world clothes for the fashion/garment industries

Carlos Hinojosa Montero

Postdoctoral Research Fellow, Image and Video Understanding Lab
Education and Early Career o Bachelor of System Engineering and Informatics, University: Universidad Industrial de Santander, 2011-2015. o Master of Science in Computer and System Engineering, University: Universidad Industrial de Santander, 2016-2018. o PhD in Computer Science, University: Universidad Industrial de Santander, 2017-2022.

Bernard Ghanem

Professor Ghanem's research focuses on computer vision and machine learning, emphasizing large-scale video understanding, vision for automated navigation and theoretical foundations.

Azza Althagafi

Ph.D. Student, Computer Science
Azza Althagafi is a Ph.D. candidate in the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Robert Hoehndorf. Professional Profile ​2016 to present Graduate Student, KAUST, Thuwal, Saudi Arabia. 2015 - 2016: Teaching Assistant, Taif University, Taif, Saudi Arabia. 2014 – 2015: Volunteer Teaching Assistant, Umm Al-Qura University, Makkah, Saudi Arabia. 2013 – 2016: Assistant Trainer, Mawhiba, The King Abdulaziz and His Companions' Foundation for Giftedness and Creativity, Makkah, Saudi Arabia. Scientific and

Amin Allam

Amin Allam obtained his Ph.D. in Computer Science at the King Abdullah University of Science and Technology (KAUST) under the supervision of Prof. Panagiotis Kalnis in 2017. Amin is a lecturer in the Computer Science department, Faculty of Computers and Information, Cairo University, Egypt. He got his bachelor's degree in Computers and Information in 2003 from Cairo University. Then, he got a Master's degree titled "An Arabic-Disambiguating Engine Based on Statistical Approaches" in Computer Science from the same university in 2008. Research Interest Amin's research interests include

Ahmed El-Roby

Ahmed El-Roby obtained his M.S. degree in Computer Science under the supervision of Professor Panagiotis Kalnis at InfoCloud Research Group (CLOUD) in King Abdullah University of Science and Technology (KAUST). He got his Ph.D. in Computer Science from the David R. Cheriton School of Computer Science at the University of Waterloo. Currently, Ahmed is working as an Assistant Professor at the School of Computer Science at Carleton University in Ottawa, Ontario. Research Interests Ahmed's research interests included Data Integration, Automatic Linking, User Feedback, Artificial Intelligence, and

Ahmed Abdulmajeed Alabdulkarim

Ahmed Abdulmajeed Alabdulkarim earned a master's degree from KAUST in 2011. His research interests are in the field of big data, artificial intelligence and machine learning, and he is now pursuing his Ph.D. at the Massachusetts Institute of Technology (MIT) in the U.S. He received his bachelor's degree with honors in computer science from King Saud University, where he graduated at the top of his class. From a young age, Alabdulkarim was passionate about computers. Studying computer science was his drive and motivation to continue his university studies. He explained that he chose KAUST for
<> bioinformatics (76)

Contributions to In Silico Genome Annotation

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B3 L5 R5209

Abstract Genome annotation is an important topic since it provides information for the foundation of downstream genomic and biological research. It is considered as a way of summarizing part of existing knowledge about the genomic characteristics of an organism. Annotating different regions of a genome sequence is known as structural annotation while identifying functions of these regions are considered as a functional annotation. In silico approaches can facilitate both tasks that otherwise would be difficult and time-consuming. This study contributes to genome annotation by introducing

Computational Methods for ChIP-seq Data Analysis and Applications

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B3 L5 5209

Abstract The development of Chromatin immunoprecipitation followed by sequencing (ChIP-seq) technology has enabled the construction of genome-wide maps of protein-DNA interaction. Such maps provide information about transcriptional regulation at the epigenetic level (histone modifications and histone variants) and at the level of transcription factor (TF) activity. This dissertation presents novel computational methods for ChIP-seq data analysis and applications. The work of this dissertation addresses four main challenges. First, I address the problem of detecting histone modifications from

Novel Computational Methods to Predict Drug–target Interactions Using Graph Mining and Machine Learning Approaches

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B3 L5 R5220

Abstract Computational drug repurposing aims at finding new medical uses for existing drugs. The identification of novel drug-target interactions (DTIs) can be a useful part of such a task. Finding computationally DTIs is a convenient strategy to identify potentially new DTIs at low cost with reasonable accuracy. However, the current DTI prediction methods suffer a high false positive prediction rate. Here, we present a comprehensive review of the recent progress in the field of DTI prediction from data-centric and algorithmic-centric perspectives that can help in constructing novel reliable

Genetic Algorithms for Optimization of Machine-learning Models and their Applications in Bioinformatics

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B3 L5 R5209

Abstract ​​​Machine-learning (ML) techniques have been widely applied to solve different problems in biology. However, biological data are large and complex, which often results in extremely intricate ML models. Frequently, these models may have poor performance or may be computationally unfeasible. This study presents a set of novel computational methods and focuses on the application of genetic algorithms (GAs) for the simplification and optimization of ML models and their applications to biological problems. The dissertation addresses the following three challenges. The first challenge is

Novel Computational Methods that Facilitate Development of Cyanofactories for Free Fatty Acid Production by Olaa Motwalli

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B3 L5 R5209

Abstract Finding a source from which high-energy-density biofuels can be derived at an industrial scale has become an urgent challenge for renewable energy production. Some microorganisms can produce free fatty acids (FFA) as precursors towards such high-energy-density biofuels. In particular, photosynthetic cyanobacteria are capable of directly converting carbon dioxide into FFA. However, current engineered strains need several rounds of engineering to reach the level of FFA production for it to be commercially viable. Thus, new chassis strains that require less engineering are needed

AI4GH Seminar Series - Machine Learning with Biological Knowledge Graphs

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B2 R5220

Abstract Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval, and federated queries. In the past years, feature learning methods that are applicable to graph-structured data are becoming available, but have not yet widely been applied and evaluated on structured biological knowledge. We develop novel methods for feature learning on biological knowledge graphs and apply them to the prediction of edges in the knowledge graph representing problems of finding candidate genes of diseases, or drugs

Neural Inductive Matrix Factorization for Predicting Disease-Gene Associations

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B3 R5208

In silico prioritization of undiscovered associations can help find causal genes of newly discovered diseases. Some existing methods are based on known associations and side information of diseases and genes. We exploit the possibility of using a neural network model, Neural Inductive Matrix Completion (NIMC) in disease-gene prediction.

AI4GH Seminar Series| Artificial Intelligence for Genomics and Health: Introduction and Overview

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B2 R5220

Abstract Genomics and health are prime areas for the development and application of novel intelligent algorithms due to a large amount of available research data, the high complexity of the problem, and the significant social and economic benefits of improving health outcomes in humans. In the past, genomics and health have been one of the key drivers in the development of Artificial Intelligence methods, including machine learning, expert systems, or graph-based algorithms. I will introduce the new seminar series on Artificial Intelligence for Genomics and Health (AI4GH), and then discuss our

AI4GH Seminar Series - Variant Prioritization in Cancer: Understanding and Prediction of Cancer Driver Genes and Mutations

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B2 R5220

Sequencing has identified millions of somatic mutations in human cancers. Identifying and distinguishing cancer driver genes amongst the millions of candidate mutations remains a major challenge.

Contributions to Computational Methods for Association Extraction from Biomedical Data: Applications to Text Mining and In Silico Toxicology

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B2 L5 R5220

Abstract The task of association extraction involves identifying links between different entities. Here, we make contributions to two applications related to the biomedical field. The first application is in the domain of text mining aiming at extracting associations between methylated genes and diseases from biomedical literature. Gathering such associations can benefit disease diagnosis and treatment decisions. We developed the DDMGD database to provide a comprehensive repository of information related to genes methylated in diseases, gene expression, and disease progression. Using DEMGD, a

Towards rational design of biosynthesis pathways

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B3 R5209

Recent advances in genome editing and metabolic engineering enabled a precise construction of de novo biosynthesis pathways for high-value natural products. One important design decision to make for the engineering of heterologous biosynthesis systems is concerned with which foreign metabolic genes to introduce into a given host organism.

Variant Prioritization in Cancer: Understanding and Prediction of Cancer Driver Genes and Mutations

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B3 R5209

Abstract Sequencing has identified millions of somatic mutations in human cancers. Identifying and distinguishing cancer driver genes amongst the millions of candidate mutations remains a major challenge. Accurate identification of driver genes and driver mutations is critical for advancing cancer research and personalizing treatment based on accurate stratification of patients. Due to inter-tumor genetic heterogeneity, many driver mutations within a gene occur at low frequencies, which make it challenging to distinguish them from other non-driver mutations. Motivated by these challenges, we

AI4GH Seminar Series - Towards Rational Design of Biosynthesis Pathways

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B2 R5220

Recent advances in genome editing and metabolic engineering enabled a precise construction of de novo biosynthesis pathways for high-value natural products. One important design decision to make for the engineering of heterologous biosynthesis systems is concerned with which foreign metabolic genes to introduce into a given host organism.

VSIM: Visualization and Simulation of Genomes for Premarital Testing

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B3 L5 R5209

Abstract Interpretation and simulation of the large-scale genomics data are very challenging, and currently, many web tools have been developed to analyze genomic variation which supports automated visualization of a variety of high throughput genomics data. We have developed VSIM an automated and easy to use web application for interpretation and visualization of a variety of genomics data, it identifies the candidate disease variants by referencing to four databases Clinvar, GWAS, DIDA, and PharmGKB, and predicted the pathogenic variants. Moreover, it investigates the attitude towards

AI4GH Seminar Series - Genome-scale Regression Analysis Reveals a Linear Relationship for Promoters and Enhancers After Combinatorial Drug Treatment

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B2 R5220

Drug combination therapy for the treatment of cancers and other multifactorial diseases has the potential of increasing the therapeutic effect while reducing the likelihood of drug resistance. In order to reduce the time and cost spent on comprehensive screens, methods are needed which can model additive effects of possible drug combinations.

AI4GH Seminar Series - VSIM: Visualization and Simulation of Genomes for Premarital Testing

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B2 R5220

Interpretation and simulation of the large-scale genomics data are very challenging, and currently, many web tools have been developed to analyze genomic variation which supports automated visualization of a variety of high throughput genomics data.

Zero in on computational challenges in Nanopore sequencing

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B9 L2 H1

In this talk, I will first give an overview of the research activities in Structural and Functional Bioinformatics Group (http://sfb.kaust.edu.sa). I will then focus on our efforts on developing computational methods to tackle key open problems in Nanopore sequencing. In particular, I will introduce our recent works on developing a collection of computational methods to decode raw electrical current signal sequences into DNA sequences, to simulate raw signals of Nanopore, and to efficiently and accurately align electrical current signal sequences with DNA sequences. Then, I will further introduce their applications in clinical and environmental fields.

Knowledge-based methods in life sciences

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B9 L2 H2 R2325

In my research I aim to understand how formalized knowledge bases can be used to systematically structure and integrate biological knowledge, and how to utilize these formalized knowledge bases as background knowledge to improve scientific discovery in biology and biomedicine. To achieve these aims, I develop methods for representing, integrating, and analyzing data and knowledge with the specific aim to make the combination of data and formalized knowledge accessible to data analytics and machine learning in bioinformatics. Biomedicine, and life sciences in general, are an ideal domain for knowledge-driven data analysis methods due to the large number of formal knowledge bases that have been developed to capture the broad, diverse, and heterogeneous data and knowledge.

Omics-based Metastasis Prediction using Machine Learning and Deep Learning

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KAUST

Knowing metastasis is the primary cause of cancer-related deaths incentivized research to unravel the complex cellular processes that drive the metastasis. Advancement in technology and specifically the advent of high-throughput sequencing provides knowledge of such processes. This knowledge led to the development of therapeutic and clinical applications. In this regard, predicting metastasis onset has also been explored using artificial intelligence (AI) approaches that are machine learning (ML), and more recently, deep learning (DL).

Professor Robert Hoehndorf's PhenomeNET method wins at Ontology matching workshop

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Professor Robert Hoehndorf and Postdoctoral Fellow Miguel Angel Rodriguez Garcia are the winners of the Ontology Alignment Evaluation Initiative Challenge sponsored by Pistoia Alliance.

Student life: studying and working at the Computational Bioscience Research Center (KAUST)

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For Ph.D. students Arturo Magana Mora, Haitham Ashoor and Olaa Motwalli, April 2017 was a particularly nerve-wracking month. After years of hard work and sleepless nights, they finally defended their thesis, bringing them one step closer to graduation.

Siqing Hou defends his Master’s thesis

1 min read ·
Siqing Hou, a Master's student under the supervision of Associate Professor Xin Gao, successfully defended his thesis.

Alumni Feature: Abdullah Khamis

1 min read ·
Abdullah Khamis has had quite an impressive career so far. After graduating with a Ph.D. degree in Computer Science from KAUST under the supervision of Professor Vladimir Bajic and Associate Professor Xin Gao, he now works as the Director of Data Science in the Center for Strategic Studies (CSS) of the Royal Court of Saudi Arabia.

Sarah Alghamdi defends her master's thesis

1 min read ·
Sarah Alghamdi successfully defended her master's thesis on April 10, 2018. Her project, entitled "Ontology Design Patterns for Combining Pathology and Anatomy: Application to Study Ageing and Longevity in Inbred Mouse Strains", was well-received by a panel of professors that included Professor Vladimir Bajic, Associate Professor Xin Gao and Assistant Professor Robert Hoehndorf.

Meshari Alazami defends his thesis proposal

1 min read ·
Meshari Alazami, a Ph.D. student under the supervision of Associate Professor Xin Gao, successfully defended his thesis proposal on May 22, 2018. His topic, "Towards Rational Design of Biosynthesis Pathways", was well received by committee members Associate Professor Xin Gao, Distinguished Professor Takashi Gojobori and Assistant Professor Robert Hoehndorf.

Maxat Kulmanov defends his thesis proposal

1 min read ·
Maxat Kulmanov, a Ph.D. candidate under the supervision of Assistant Professor Robert Hoehndorf, defended his thesis proposal on July 31, 2018.

Computing solutions for biological problems

1 min read ·
Xin Gao’s research is building bridges between computer science and biology.

Meshari Alazmi defends his Ph.D. thesis

1 min read ·
Meshari Alazmi, a Ph.D. candidate under the supervision of Associate Professor Xin Gao, defended his Ph.D. dissertation on October 24, 2018.

Azza Althagafi defends her Master's thesis

1 min read ·
Azza Althagafi, a student under the supervision of Assistant Professor Robert Hoehndorf, defended her Master's thesis on November 1, 2018.

Sara Althubaiti defends her Master's thesis

1 min read ·
Sara Althubaiti, a student under the supervision of Assistant Professor Robert Hoehndorf, defended her Master's thesis on November 5, 2018.

Robert Hoehndorf promoted to associate professor at KAUST

1 min read ·
KAUST faculty member Robert Hoehndorf was recently promoted from the rank of assistant professor to associate professor. Hoehndorf’s promotion caps a year-long process where the German researcher’s scientific and scholarly output was measured and evaluated by internal and external reviews.

Talented KAUST Ph.D. student aims to revolutionize healthcare with machine learning

1 min read ·
KAUST Ph.D. student, Yu Li, is a talented young computer scientist with an interest in developing novel computational methods and algorithms to solve and understand the principles behind the “bio-world.”

Xin Gao promoted to rank of professor

1 min read ·
Computational Bioscience Research Center (CBRC) would like to congratulate Xin Gao for being promoted to the rank of Professor, effective January 1, 2021. Prof. Gao joined KAUST in October 2010 and became a CBRC PI in January 2013. He has since then produced tremendous work in the areas of computational biology, bioinformatics, machine learning, and algorithms. Dr. Xin Gao is also the Acting Associate Director of the Computational Bioscience Research Center and the lead of the Structural and Functional Bioinformatics Group at KAUST. Prior to joining KAUST, he was a Lane Fellow at Lane Center

KAUST alumnus named in Forbes' “30 Under 30 Asia list” – Class of 2022

1 min read ·
KAUST alumnus Yu Li (Ph.D. '20) was recently named amongst the Forbes' "30 Under 30 Asia list" – Class of 2022. Forbes recognized the current assistant professor of computer science and engineering at The Chinese University of Hong Kong (CUHK) for his work developing algorithms to solve problems in biology and health care.

AI tool predicts function of unknown proteins

1 min read ·
Powered by logic and large language models, DeepGO-SE predicts the biological role of proteins and could be used to aid drug discovery.
KAUST-CEMSE-CS-SFB-Zhongxiao-Li

Zhongxiao Li

Ph.D. Student, Computer Science
Zhongxiao Li is a Ph.D. student under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST).​ Zhongxiao obtained his Bachelor of Science degree from Southern University of Science and Technology, China in 2018. Research Interests Zhongxiao's research interests include Bioinformatics and Computational Biology.

Yongkang Long

Ph.D. Student, Computer Science
Yongkang Long is a Ph.D. student under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Research Interests Yongkang's research interests include Bioinformatics and Computational Biology.

Yu Li

Ph.D. Student, Computer Science
Research Interests Yu Li is a Ph.D. student at KAUST, majoring in Computer Science. His advisor is Prof. Xin Gao. His research interests are Deep Learning, Machine Learning, and Bioinformatics. He got his Master degree in Computer Science at KAUST in December 2016. Before that, he got his Bachelor degree in Biosciences at the University of Science and Technology of China (USTC). Scientific and Professional Membership Association for the Advancement of Artificial Intelligence(AAAI) Member International Society for Computational Biology (ISCB) Member Education Profile Ph.D student, Computer

Yang Liu

Ph.D. Student, Computer Science
Yang Liu is an M.S./Ph.D. candidate in the KAUST Bio-Ontology research group under the supervision of Professor Robert Hoehndorf. Before joining KAUST. Research Interests Yang's interests include bioinformatics, machine learning and human genomics. She is interested in building models for human disease diagnosis and prognosis, especially for cancer, and applying statistical methods to analyze human omics data. Education Profile B.Sc, Bioengineering, Harbin Institute of Technology, Harbin, China, 2016-2020. M.Sc, Bioengineering, King Abdullah University of Science and Technology, Thuwal, Saudi

Xin Gao

Program Chair, Computer Science
Professor Gao is a world-leading expert in developing novel AI solutions for challenges in biology, biomedicine, and health and wellness, with a focus on AI-based drug development, large language models in biomedicine, biomedical imaging analysis, and omics-based disease detection and diagnostics.

Xiaopeng Xu

Ph.D. Student, Computer Science
Xiaopeng Xu is a Ph.D. candidate in the KAUST Structural and Functional Bioinformatics group under the supervision of Professor Xin Gao. Before joining Ph.D. program, Xiaopeng obtained a master’s degree in computer science from King Abdullah University of Science and Technology (KAUST), and a bachelor’s degree in bioinformatics from Huazhong University of Science and Technology, respectively. Research Interests Xiaopeng's research interests included Bioinformatics, artificial intelligence and computational biology.

Wenkai Han

Ph.D. Student, Computer Science
Wenkai Han is a Ph.D. student under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Wenkai obtained his Bachelor of Science degree from the University of Science and Technology of China in 2018. Research Interests Wenkai's research interests include Bioinformatics and Computational Biology.

Trisevgeni Rapakoulia

Trisevgeni Rapakoulia obtained her Ph.D. degree (2016-2019) in Computer Science under the supervision of Professor Xin Gao at structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Research Interests Trisevgeni's research interests include bioinformatics, System Biology, Prediction of harmful SNPs, Genome-Wide Association Studies and Artificial Intelligence, Machine Learning Approaches, Evolutionary Algorithms, Feature Selection Techniques. Research field: study the effects of drugs and drug combinations in the transcriptome level

Vasiliki Kordopati

Vasiliki Kordopati obtained Master degree in Computer Science under the co-supervision of Professor Boon Ooi at Photonics Laboratory and Prof. Xin Gao at Structural and Functional Bioinformatics Group (SFB) from King Abdullah University of Science and Technology (KAUST). Vasiliki is a co-founder of Oæsis, a King Abdullah University of Science and Technology (KAUST) startup spinout, where she is responsible for product development. A computer engineer by training, she received a bachelor's degree in Computer Engineering from the University of Patras, Greece. Research Interest Vasiliki's

Tilman Hinnerichs

Visiting Student, Bio-Ontology Research Group
Tilman Hinnerichs was a visiting student at Professor Robert Hoehndorf's Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Research Interests Tilman's research interests included ​Machine Learning, Bioinformatics, Ontologies, Reasoning, Logic, Particle Methods. He was particularly interested in the application of machine-learning algorithms to problems of the bioscience field. Education Profile ​Since 2016: Diploma Student, Computer Science, TU Dresden​. 2015-2016: Student, Human Medicine, Charité Berlin.

Somayah Albaradei

Ph.D. Student, Computer Science
Somayah Albaradei is a Ph.D. student at Structural and Functional Bioinformatics Group (SFB) under the supervision of Professor Xin Gao at King Abdullah University of Science and Technology (KAUST). ​Somayah obtained her master's degree in computer science from the University of Manitoba, Canada in 2014. Professional Profile 2014: Lecturer at College of Computing and Information Technology-King Abdulaziz University - Jeddah - Saudi Arabia 2010- 2011: Teacher Assistant at College of Computing and Information Technology-King Abdulaziz University - Jeddah - Saudi Arabia 2009-2010: Collaborator

Siqing Hou

Siqing Hou obtained his Master's degree in Computer Science under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Master Thesis, 2018: Neural Inductive Matrix Completion for Predicting Disease-Gene Associations Research Interests Siqing Hou was working in the fields of Bioinformatics, Computational Biology and Machine Learning. Professional Profile Research Student, University of Birmingham, Jul 2015 - Aug 2018 Software Effort Estimation, Machine Learning Education Profile M.Sc. Computer

Sheng Wang

Sheng Wang worked as a Research Scientist at Prof. Xin Gao's Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Sheng got his Ph.D. in Bioinformatics from the Institute of Theoretical Physics at the Chinese Academy of Sciences in 2010. He was then a postdoc fellow at Toyota Technological Institute in Chicago during 2010 and 2014. During 2014 and 2017, he was a research professional at the Department of Human Genetics at the University of Chicago. Since March 2017, he joined the SFB group as a research scientist. Currently, Sheng is a

Shahad Qathan

M.S. Student, Computer Science
Shahad Qathan is an MS candidate in the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Robert Hoehndorf. Research Interests Artificial Intelligence, more specifically Machine Learning and its applications in healthcare and Bioinformatics Education Profile B.Sc, Computer Science, King Abdulaziz University, Jeddah, Saudi Arabia, 2020

Senay Kafkas

Research Scientist, Bio-Ontology Research Group
Research Interests Senay's research interests include biomedical text/data mining, Natural Language Processing (NLP), and Next Generation Sequencing (NGS) data analysis. Scientific and Professional Membership ​International Society of Computational Biology (ISCB) (2010-present). KAUST Affiliations Bio-Ontology Research Group (BORG).​ Computational Bioscience Research Center (CBRC). Computer, Electrical, and Mathematical Sciences and Engineering (CEMSE). Education Profile Ph.D., Computer Engineering, Eastern Mediterranean University, Cyprus, 2012. M.Sc., Computer Engineering, Eastern

Sara Althubaiti

Ph.D. Student, Computer Science
Sara Althubaiti is currently a Ph.D. candidate in the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Robert Hoehndorf. Prior to this, Sara received her master's degree in computer science with a focus on bioinformatics from KAUST in December 2018. Research Interests Sara's research interests include bioinformatics, text mining, ontologies, and cancer. Her research focuses on applying machine learning methods in cancer biology and development specifically in the field of finding driver genes and mutations in

Robert Hoehndorf

Associate Professor, Computer Science
Robert Hoehndorf is an Associate Professor of Computer Science at KAUST. His research focuses on bioinformatics, knowledge representation and reasoning, Semantic Web technologies, and neuro-symbolic methods.

Renmin Han

Postdoctoral Research Fellow, Structural and Functional Bioinformatics
Renmin Han is a Research Scientist at the Structural and Functional Bioinformatics Group (SFB) of Professor Xin Gao at King Abdullah University of Science and Technology (KAUST). ​Renmin obtained his Ph.D. degree in Computer Science from the Institute of Computing Technology, Chinese Academy of Sciences, China. Research Interests ​Renmin is interested in the development of computer science. Currently, he is working on imaging processing in electron micrography, especially for the reconstruction of biological ultrastructures. His work is to use computational methods to improve the boundary of

Rahaf Shiqdar

Visiting Student, Bio-Ontology Research Group
Rahaf Shiqdar was a visiting student from ​Umm Al-Qura University ​at Professor Robert Hoehndorf's Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST). Research Interests Rahaf's research interests included ​Bioinformatics, Machine and Deep learning.

Peng Yang

Postdoctoral Research Fellow, Structural and Functional Bioinformatics
Peng Yang worked as a Postdoctoral research fellow at Prof. Xin Gao's Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST) during 2016-2018. Currently, Peng is a research scientist at Baidu Research. Research Interests ​Machine Learning and Data Mining problem, especially online active learning, multi-task learning, recommender systems, and deep learning. Algorithms for bioinformatics problems, especially in disease gene prediction, drug-target interaction prediction, protein complex, and microbial community pattern detection

Othman Soufan

Research Interests I’m a Ph.D. in Computer Science; currently focusing on developing drug discovery and biomedical applications through data mining and graph mining techniques. I excel in analyzing data, extracting unseen useful patterns and achieving a better informative decision-making process. The result of my work can be found in one filed patent and several peer-reviewed publications in international journals ranked top in the respective domain of specialty. I have a strong business background, both academic and empirical, derived from my education and family business. Being transparent

Maxat Kulmanov

Research Scientist, Bio-Ontology Research Group
Maxat Kulmanov is a Research Scientist in the Bio-Ontology Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Maxat's research interests include bioinformatics, knowledge representation and reasoning, machine learning, neural networks, semantic web, and algorithms. He is interested in knowledge discovery and data integration using artificial intelligence and semantic web technologies in biology and biomedicine. Professional Profile 2020-Present: Postdoctoral Research Fellow, Computational Bioscience Research Center, King Abdullah University of

Mario Messih

Mario Messih is a Former Visiting student from the Sapienza University of Rome, Italy at Professor Xin Gao's Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Mario Finished his Ph.D. study with Prof. Anna Tramontano at the Sapienza University of Rome, Italy, and currently, Mario is a postdoc fellow at Yale University. Research Interests Mario's research interests included Bioinformatics and computational biology. His main interests lie at the intersection of machine learning, statistics and their applications on analyzing large

MD Nurul Muttakin

Ph.D. Student, Computer Science
Nurul Muttakin, is an M.S./Ph.D. candidate in the Bio-Ontology Research Group (BORG) under the supervision of Professor Robert Hoehndorf. Before joining KAUST Nurul completed his B.Sc. in CSE from Bangladesh University of Engineering and Technology, Bangladesh. Research Interest Nurul is Interested in the application of Artificial Intelligence in Bioinformatics tasks like protein function prediction as well as understanding the theory behind it. Education Profile B.Sc. in CSE, Bangladesh University of Engineering and Technology, Bangladesh

Maha Thafar

Ph.D. Student, Computer Science
Research Interests I am a computer scientist focusing on developing novel computational methods using artificial intelligence, machine learning, deep learning, and graph mining techniques to solve biomedical problems. I am developing these methods to be applied to large-scale chemical and biomedical data. Currently, I'm working on compound-protein interactions prediction and binding affinity prediction projects. My objective is to empower women in my country with a flourishing academic background that enables building better careers for themselves. Additionally, to create a research culture

Luke Slater

Ph.D. Student, Computer Science
Luke Slater is working as a Research Fellow in the Institute of Cancer and Genomics with an interest in ontology and ontology accessories, and their combination with other interdisciplinary techniques to gain novel and practically useful insights into information.

Kexin Niu

Ph.D. Student, Bioengineering
Kexin Niu, is an M.S./Ph.D. candidate in the Bio-Ontology Research Group (BORG) under the supervision of Professor Robert Hoehndorf. Before joining KAUST Kexin obtained her bachelor's degree in Bioinformatics from Southern University of Science and Technology, Shenzhen, China. "What I like best about KAUST is the communication of diverse cultures and diverse disciplines, which enables me to have more possibilities." Research Interest Kexin’ s Research interests include but are not limited to bioinformatics and application of machine learning method in biomedical data. Education Profile 2016 -

Jun Chen

Ph.D. Student, Computer Science
Jun Chen is a Ph.D. candidate in the Computer Science Program under the supervision of Professor Mohamed Elhoseiny at King Abdullah University of Science and Technology (KAUST). Education and Early Career Jun obtained his bachelor's degree in Computer Science from Xi’an Jiaotong-Liverpool University in China, 2018. He joined KAUST as a Master student under the supervision of Prof. Robert Hoehndorf and received his master's degree in Computer Science in 2019. Research Interests Jun's research interests include machine learning, artificial intelligence, natural language processing, knowledge

Heba Abu Samra

Heba Abu Samra obtained her Master's degree in Computer Science under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Master Thesis (2013): A Comparative Study of Feature Selection and Classification Methods for Gene Expression Data Research Interests Heba's research interests included Bioinformatics and Computational Biology.

Haoyang Li

Ph.D. Student, Computer Science
Haoyang Li is a Ph.D. student in the KAUST Structural and Functional Bioinformatics research group under the supervision of Professor Xin Gao. Before joining KAUST, Haoyang majored in Bioinformatics and graduated from Jilin University (JLU), located in Changchun, Jilin, China. Research Interests Haoyang focuses on computational biology and bioinformatics. As a visiting student before, Haoyang did some research in the field of medical image analysis and metabolic reprogramming. Recently, Haoyang focuses on spatial transcriptomics techniques and has an interest in developing related methods.

Haitham M. Ashoor

Research Interests Haitham Ashoor is a Ph.D. candidate in the Computer Science program at King Abdullah University of Science and Technology (KAUST). He is working under the supervision of Prof. Vladimir Bajic. He has obtained his BSc degree in Computer Engineering from the University of Jordan, Amman Jordan in 2008. He joined KAUST as an MSc student in 2009. In 2011, He finished his master studies under the supervision of Prof. Vladimir Bajic. His main research interests are developing computational methods for next-generation sequencing data analysis and applications of machine learning in

Gian Marco Messa

Ph.D. Student, Computer Science
Gian Marco is a Ph.D. student at the Structural and Functional Bioinformatics Group (SFB) under the supervision of Professor Xin Gao at King Abdullah University of Science and Technology (KAUST). Gian Marco, upon finishing high school in Lecce (Italy), applied to the Politecnico of Milan in the year 2009 in Physics engineering. Due to personal issues, he came back to his hometown to complete the Bachelor's Degree at the University of Salento in Biotechnology. For his master's degree, seeking innovation and cutting edge knowledge, he moved to the University of Bologna, which offered an M. Sc

Fernando Patricio Zhapa-Camacho

Ph.D. Student, Computer Science
Fernando Patricio Zhapa Camacho is an M.S./Ph.D. candidate in the KAUST Bio-Ontology research group under the supervision of Professor Robert Hoehndorf. Before joining KAUST, Fernando obtained a bachelor's degree in Information Technology from Yachay Tech University, Ecuador. Research Interests Fernando’s research interests include knowledge representation and reasoning, machine learning and bioinformatics. He is interested in artificial intelligence and logic integration with applications in biology. Professional Profile: • 2020 to present: Graduate Student, KAUST, Thuwal, Saudi Arabia. •

Francesco Napolitano

​Francesco is a Computer Scientist with a background in Mathematical Models and Machine Learning. He focused his M.Sc. (University of Salerno, SA, Italy) and Ph.D. (University of Salerno and the University of California, Irvine, CA, USA) on supervised and unsupervised data analysis, particularly for clustering of complex, high-dimensional data. He developed and applied data analysis techniques to problems from different fields, including fault detection in avionics and computational pharmacology, before focusing on bioinformatics and systems biology. Research Interests Francesco's main

Dimitrios Kleftogiannis

Dimitrios Kleftogiannis obtained his Ph.D. degree in Computer Science under the supervision of Professor Panagiotis Kalnis at InfoCloud Research Group in King Abdullah University of Science and Technology (KAUST), in 2016. Research Interests Dimitrios works mostly on knowledge discovery technologies with applications in Bioinformatics and Computational Biology. ​His research interests include Machine Learning, Artificial Intelligence and Data Analysis of NGS-derived data. He has worked extensively on the prediction of distal DNA regulatory elements focusing on the category of enhancers. He is

Deng Luo

Ph.D. Student, Computer Science
Deng Luo is a Ph.D. Candidate in the Computer Science program under the supervision of Professor Ivan Viola at the Visual Computer Center (VCC) at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia. Education and Early Career Deng Luo earned his Bachelor's degree in Bioinformatics from Southern University of Science and Technology in Shenzhen, China in 2016. He has a multidisciplinary background, including bioinformatics, molecular biology, computer science, and entrepreneurship. He joined KAUST and received his Master's degree in Bioscience in 2019. Deng was trained as

Arwa Bin Raies

Research Interests I am a computer scientist whose primary focus is on developing novel machine learning methods to extract useful information from complex datasets. My experience, however, has covered intellectually wide-ranging areas spanning several disciplines in computer science, biology, and chemistry. At the undergraduate level, I focused on computer networks, and I did an internship for six months at Cisco Systems Inc., and I achieved CCNA and CCNP certification. At the Master's level, however, I joined the Computational Bioscience Research Center. I was in charge of a text-mining

Ahmed Abbas

Ahmed Abbas obtained his Ph.D. degree under the supervision of Prof. Xin Gao at Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Currently, Ahmed is an Assistant Professor at Ain Shams University, Egypt. Research Interests Ahmed's research interests included Bioinformatics, NMR protein structure determination. Selected Publications 1. Carlo Cannistraci, Ahmed Abbas, and Xin Gao. (2015). “Median modified Wiener filter for nonlinear adaptive spatial denoising of protein NMR multidimensional spectra”. Scientific Reports (Nature

Abeer Almutari

M.S. Student, Computer Science
Abeer Almutari obtained her Master's degree in Computer Science under the supervision of Professor Robert Hoehndorf at Bio-Ontology Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Abeer's research interests included bioinformatics, machine learning and building new automated models using artificial intelligence. Education ​B.Sc. Electrical & Computer Engineering, King Abdul-Aziz University, Jeddah, Saudi Arabia, 2016

Abdullah Khamis

Abdullah Khamis graduated with a Ph.D. degree in Computer Science from KAUST under the supervision of Professor Vladimir Bajic and Associate Professor Xin Gao. He now works as the Director of Data Science in the Center for Strategic Studies (CSS) of the Royal Court of Saudi Arabia. He leads the Data Science team in developing several machine learning based predictive models such as sentiment analysis and prediction. Research Interest Abdullah's research interests were in bioinformatics and machine learning.

Abdullah Muhammad Kamal

Visiting Student, Computer Science
Abdullah Muhammad-Kamal is a visiting student from the University of Warwick, United Kingdom at (BORG) Bio-Ontology Research Group of Professor Robert Hoehndorf at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Currently on his placement year, interning with the Bio-Ontology Research Group at KAUST, he will be graduating with his Bachelor's degree in Computer and Management Sciences in the summer of 2021. Research Interests ​Abdullah's research interests include Bioinformatics, Machine Learning, and Data Science.
<> Deep learning (58)

Karen Sanchez

Postdoctoral Research Fellow
Karen Sanchez is a Postdoctoral Researcher in the IVUL lab at KAUST, specializing in deep learning, machine learning, and artificial intelligence (AI) for healthcare applications. Her research focuses on video understanding, domain adaptation, generative AI, and methods for preserving patient privacy.

Ebrahim Al-Safadi

Intern, Information Science Lab at King Abdullah University of Science and Technology (KAUST),

Mhd. Emad Alolaby

Sixiu Liu

Ph.D. degree in Earth science and engineering with the King Abdullah University of Science and Technology (KAUST)

Ahmed Youssef Ragab Radwan

Visiting Student (former), Information Science Lab
Visiting student at the Information Science Lab, in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST)

Advanced Breach and Attack Simulation using ML

Cybersecurity is becoming a need more than ever. Organizations need to protect their digital assets and are required to earn certifications to prove the compliance to the regulations and rules. For this, these assets must be assessed to ensure the target security posture and to get certified or pass an audit on yearly basis. This is a daunting and costly task as it often requires a third-party tester that tries to penetrate the system, under agreement. Breach and Attack Simulation is a new method that allows to do this penetration testing in-house, using some automation tools. Some of these

Carlos Hinojosa Montero

Postdoctoral Research Fellow, Image and Video Understanding Lab
Education and Early Career o Bachelor of System Engineering and Informatics, University: Universidad Industrial de Santander, 2011-2015. o Master of Science in Computer and System Engineering, University: Universidad Industrial de Santander, 2016-2018. o PhD in Computer Science, University: Universidad Industrial de Santander, 2017-2022.
<> Computer science (57)

PCCFD - Predictive Complex Computational Fluid Dynamics

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B9 L2 H1

The PCCFD workshop will focus on cutting-edge research in the field of algorithmic development for CFD and multi-scale complex flow simulations.

Reproducibility in Computer Science - Part 1

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Reproducibility in Computer Science - Part 2

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Reproducibility in Computer Science - Part 3

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Knowledge-based methods in life sciences

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B9 L2 H2 R2325

In my research I aim to understand how formalized knowledge bases can be used to systematically structure and integrate biological knowledge, and how to utilize these formalized knowledge bases as background knowledge to improve scientific discovery in biology and biomedicine. To achieve these aims, I develop methods for representing, integrating, and analyzing data and knowledge with the specific aim to make the combination of data and formalized knowledge accessible to data analytics and machine learning in bioinformatics. Biomedicine, and life sciences in general, are an ideal domain for knowledge-driven data analysis methods due to the large number of formal knowledge bases that have been developed to capture the broad, diverse, and heterogeneous data and knowledge.

On Studying Computer Science - 2020-03-16

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KAUST

In this talk, I will discuss a wide range of ideas on studying computer science and doing research in computer science. Peter Wonka is the program chair of the computer science program. His research interests are deep learning, computer graphics, computer vision, and remote sensing.

On Studying Computer Science - 2020-09-07

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KAUST

The talk will be pre-recorded and a youtube link will be provided to seminar participants. During the seminar time, Prof. Wonka will mainly answer some questions from the audience.

RC3 Advisory Board Distinguished Lectures Workshop

RC3 Advisory Board

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B5 L5 R5220

Machine learning (ML) has witnessed remarkable advancements in recent years, demonstrating its effectiveness in a wide array of applications, including intrusion detection systems (IDS). However, when operating in adversarial environments, ML-based systems are susceptible to a range of attacks.

Finding the Face in the Crowd

1 min read ·
An algorithm that maximizes the difference between data categories achieves high accuracy in classifying faces.

Disease researchers have a way with words

1 min read ·
Specialized word search method sheds light on phenotypic similarity of diseases.

Honey bee behavior is in the genes

1 min read ·
An integrated set of genetic mechanisms controls whether bees behave as foragers or nurses.

Choosing the right scheme for green communications

1 min read ·
Performance analysis reveals how different resource allocation schemes may impact the energy efficiency of a cognitive radio system.

Unlocking complex interactions

1 min read ·
Functions of interacting molecular complexes are uncovered through a novel method of aligning the interaction interface of each complex.

Noisy cells produce bursts of protein

1 min read ·
A new mathematical model explains how random factors affect the production of proteins within the cells.

Sharing movement aids learning

1 min read ·
Automated learning of an individual’s movement patterns shared over mobile and social networks could help us to connect better.

Exploring protein form to find function

1 min read ·
Existing systems for predicting protein structure are outperformed by a newly developed method.

Exploring artificial biosynthesis

1 min read ·
Metabolic route explorer helps to optimize the pathways for artificial biosynthesis of valuable products.

Filling in the blanks of virtual cities

1 min read ·
New software generates three-dimensional models of buildings, with applications for disaster relief support.

Technology search for relationships

1 min read ·
A sketch-based query for searching for relationships among objects in images could enhance the power and utility of image search tools.

Divide and conquer pattern searching

1 min read ·
Searching for recurring patterns in network systems has become a fundamental part of research and discovery in fields as diverse as biology and social media. KAUST researchers have developed a pattern or graph-mining framework that promises to significantly speed up searches on massive network data sets. “A graph is a data structure that models complex relationships among objects,” explained Panagiotis Kalnis, leader of the research team from the KAUST Extreme Computing Research Center. “Graphs are widely used in many modern applications, including social networks, biological networks like

Algorithm scours datasets to diagnose medical mysteries

1 min read ·
A new tool uses genetic and clinical information to find the root cause of unexplained illnesses.

Sharing expert experimental knowledge to expedite design

1 min read ·
A repository of metabolic information provides a quick reference tool for designing useful synthetic biological systems.

Streamlining space structures

1 min read ·
A new computational framework marries ideas from computer graphics with civil engineering to reduce times and costs of constructing frame-based structures.

Xiangliang Zhang joins CBRC

1 min read ·
Xiangliang Zhang, KAUST associate professor of computer science and principal investigator of the Machine Intelligence & Knowledge Engineering Lab, joined the Computational Bioscience Research Center (CBRC) on July 1, 2018. In CBRC, Xiangliang will work on the problems of modeling biological networks using machine learning models.

Polymers offer a better view

1 min read ·
A window opens for analyzing the distribution of small molecules in biological and medical tissue samples.

Querying big data just got universal

1 min read ·
A universal query engine for big data that works across computing platforms could accelerate analytics research.

Mastering a prickly problem in ferrofluids

1 min read ·
Computer simulation accurately captures the beguiling motion of a liquid magnetic material.

AI learns complex gene-disease patterns

1 min read ·
A deep learning model improves the ability to identify genes potentially involved in disease.

Database to support infectious disease research

1 min read ·
Linking disease pathogens to clinical signs and symptoms through a database could support research into the molecular mechanisms of infectious diseases.

Less chat leads to more work for machine learning

1 min read ·
Deep analysis of the way information is shared among parallel computations increases efficiency to accelerate machine learning at scale.

Symbol of change for AI development

1 min read ·
Bridging the knowledge gap in artificial intelligence requires an embedding function that helps step between different types of "thinking."

Machine learning models gather momentum

1 min read ·
New methods for training machine learning models are quicker and more accurate than current approaches, previously considered state-of-the-art.

AI reveals nature of RNA-protein interactions

1 min read ·
A deep learning tool could help in structure-based drug discovery.

Algorithm turns cancer gene discovery on its head

1 min read ·
A method for finding genes that spur tumor growth takes advantage of machine learning algorithms to sift through reams of molecular data collected from studies of cancer cell lines, mouse models and human patients.

AI uses your imagination

1 min read ·
The psychology of human creativity helps artificial intelligence imagine the unseen.

Peeling back the layers of deep machine learning

1 min read ·
A layer-based approach raises the efficiency of training artificial intelligence models.

Training agents to walk with purpose

1 min read ·
By training a search agent to make smarter exploratory decisions, relational data can be classified more accurately and efficiently.

Angling for underwater WiFi

1 min read ·
Scuba divers could send sea life shots in real time using an aquatic internet service.

Computers excel in chemistry class

1 min read ·
Machine learning models can rapidly and accurately estimate key chemical parameters related to molecular reactivity.

Peering under the hood of SARS-CoV-Two

1 min read ·
Microscope and protein data are incorporated into an easy-to-use-and-update tool that can model an organism’s 3D appearance.

Ferrofluid surface simulations go more than skin deep

1 min read ·
Computer models efficiently and accurately simulate the magnetic responses of ferrofluids by considering only the fluid’s surface.

Simulating evolution to understand a hidden switch

1 min read ·
Some organisms evolve an internal switch that can remain hidden for generations until stress flicks it on.

Xin Gao promoted to rank of professor

1 min read ·
Computational Bioscience Research Center (CBRC) would like to congratulate Xin Gao for being promoted to the rank of Professor, effective January 1, 2021. Prof. Gao joined KAUST in October 2010 and became a CBRC PI in January 2013. He has since then produced tremendous work in the areas of computational biology, bioinformatics, machine learning, and algorithms. Dr. Xin Gao is also the Acting Associate Director of the Computational Bioscience Research Center and the lead of the Structural and Functional Bioinformatics Group at KAUST. Prior to joining KAUST, he was a Lane Fellow at Lane Center

First 3D Visualizations of SARS-CoV-2 Cryo-EM Tomography Data

1 min read ·
Together with our partners from Tsinghua University and Nanographics, our group has succeeded in depicting the first 3D visualizations of Cryo-EM microscopy tomography directly from the data. We are advancing the technology to allow for clear, noise-free visualization of this challenging data modality. Once we succeed with that our next challenge is to integrate such 3D visualization into the structural modeling process. Stay tuned!

Cloud simulations get a dose of realism

1 min read ·
A focus on the fundamental physics of cloud formation leads to highly realistic simulations of different types of clouds.

Machine learning at speed

1 min read ·
Optimizing network communication accelerates training in large-scale machine-learning models.

The IEEE CS Global Student Challenge seeks top KAUST CS talent

1 min read ·
The annual IEEE Computer Society Global Student Challenge provides an avenue for students worldwide to create innovative solutions to big data problems. This year’s competition, IEEE CS GSC 2021, encourages each individual or team (max three students) to present solutions to pre-determined problem/issue statement of relevance in CS today.

Training an AI eye on the Moon

1 min read ·
Machine learning accelerates the search for promising Moon sites for energy and mineral resources.

Viruses as communication molecules

1 min read ·
Electrical and computer engineers take on complex modeling questions that can further our understanding of virus spread in small spaces.

Wildfire simulation heats up

1 min read ·
Computational models capture the capricious behavior of forest fires.

Harnessing the particle-tracking power of the algorithm

1 min read ·
A simple camera system paired with a sophisticated image-processing algorithm can achieve faster and more accurate reconstructions of particle flow.

Drop the zeros for a performance boost

1 min read ·
An eight-fold speed up of deep machine learning can be achieved by skipping the transmission of zero values.

New database to "SpUR" on cancer research

1 min read ·
Xin Gao's team developed an interactive web portal where cancer scientists can interrogate how RNA splicing in noncoding parts of genes fuels the growth of different types of tumors.

Computer learns to write captions with feeling

1 min read ·
Mohamed Elhoseiny and his PhD student Youssef Mohamed are teaching AI to capture the full spectrum of human emotions when annotating artwork in order to reduce emotional bias in computer-generated captions.

AI helps diagnose post-COVID lung problems

1 min read ·
A new AI diagnostic tool developed by KAUST scientists allows doctors to visualize lung damage caused by COVID-19 in more detail.

Watching viscous flow, but faster

1 min read ·
A state-of-the-art method for modeling the behavior of liquids described by the Computational Sciences Group represents a breakthrough in computational speed for viscous liquids.

Razen Al Harbi

Razen Al Harbi obtained his Ph.D. degree in Computer Science under the supervision of Professor Panagiotis Kalnis at InfoCloud Research Group (CLOUD) in King Abdullah University of Science and Technology (KAUST) in 2016. Research Interests Razen's research interests included ​Large scale data management with an emphasis on managing and querying large-scale graphs. Selected Publications Ali H. Dogru, Larry S. K. Fung, Usuf M, Tareq M. Al-Shaalan, Jorge A. Pita, K. Hemanthkumar, H. J. Su, James C. Tan, H. Hoy, W. T. Dreiman, W. A. Hahn, R. Al-Harbi, A. Al-Youbi, N. M. Al-Zamel, M. Mezghani “A
<> Computer Vision (55)

Karen Sanchez

Postdoctoral Research Fellow
Karen Sanchez is a Postdoctoral Researcher in the IVUL lab at KAUST, specializing in deep learning, machine learning, and artificial intelligence (AI) for healthcare applications. Her research focuses on video understanding, domain adaptation, generative AI, and methods for preserving patient privacy.

Aznaur Aliev

M.S. Student

Hani A. Al Majed

M.S. Student
Electrical and Computer Engineering Master's student at KAUST under the supervision of Professor Bernard Ghanem, with expertise in machine learning workflow automation, physics-informed neural operators, and AI for Science, particularly in chemistry applications. Passionate about leveraging AI to solve complex scientific problems.

Ahmad Sait

M.S. Student, Computer Science
Ahmad Sait is a Master's student in the Computer Science program at KAUST. Before joining KAUST, Ahmad obtained his Bachelor's degree in Computer Science from King Abdulaziz University, Saudi Arabia. Ahmad is also a KAUST Academy graduate from the AI track, having been under the supervision of Prof. Naeemullah Khan. The Academy provided him with seven opportunities to assist in teaching students enrolled in subsequent cohorts.

Stylistic and spatial disentanglement in GANs

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KAUST

This dissertation tackles the problem of entanglement in Generative Adversarial Networks (GANs). The key insight is that disentanglement in GANs can be improved by differentiating between the content, and the operations performed on that content. For example, the identity of a generated face can be thought of as the content, while the lighting conditions can be thought of as the operations.

Rising Stars in AI Symposium 2022 at KAUST

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B4 B5 A0215

Rising Stars in AI Symposium 2022 at KAUST The "Rising Stars in AI Symposium" is geared towards young researchers (including PhD students, PostDocs, young faculty) who have recently published promising work at leading venues. There will be dozens of brief in-person presentations about papers recently accepted at major AI conferences such as NeurIPS, CVPR, EMNLP, ACL, ICML, ICLR, etc. All speakers will be encouraged to start with an intro for non-AI experts. To view the complete program and for more event details, please visit the symposium website. Attend the Symposium The symposium will be

Omics-based Metastasis Prediction using Machine Learning and Deep Learning

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KAUST

Knowing metastasis is the primary cause of cancer-related deaths incentivized research to unravel the complex cellular processes that drive the metastasis. Advancement in technology and specifically the advent of high-throughput sequencing provides knowledge of such processes. This knowledge led to the development of therapeutic and clinical applications. In this regard, predicting metastasis onset has also been explored using artificial intelligence (AI) approaches that are machine learning (ML), and more recently, deep learning (DL).

Towards Detailed Understanding of the Visual World

Fahad Khan, Associate Professor at MBZUAI and Linköping University

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B9 L2 R2322

Machine perception that corresponds to the ability to understand the visual world based on the input from sensors, such as cameras is one of the central problems in Artificial Intelligence. To this end, recent years have witnessed tremendous progress in various instance-level recognition tasks having real-world applications in e.g., robotics, autonomous driving and surveillance. In this talk, I will first present our recent results towards understanding state-of-the-art deep learning-based visual recognition networks in terms of their robustness and generalizability. Next, I will present our results on learning visual recognition models with limited human supervision. Finally, I will discuss moving one step further from instance-level recognition to understand visual relationships between object pairs.

Bridging the Gap between the Academia and Industry by Extending High-Quality Research to Practical Business Needs

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B9 L2 R2322 H1

This seminar focuses on providing the audience with the context and scope of our internship program. The program is for the young and talented graduate students with an active interest in solving real-world problems. Some of the projects that will be presented in the seminar are actively developed in Elm and include domains such as computer vision, robotics and automation, healthcare, IoT, video analytics, and NLP. The seminar will serve as a launch pad to allow students to discuss their future interests and aspirations with the speaker. It will also enable them to develop a better awareness of domains more relevant to their future research aspirations.

Towards Richer Video Representation for Action Understanding

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B2 L5 R5209

With video data dominating the internet traffic, it is crucial to develop automated models that can analyze and understand what humans do in videos. Such models must solve tasks such as action classification, temporal activity localization, spatiotemporal action detection, and video captioning. This dissertation aims to identify the challenges hindering the progress in human action understanding and propose novel solutions to overcome these challenges.

Best Business Plan at VIEW 2016

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Hachid won the award for the Venture pitch with the best business plan at the Fifth Annual Vision Industry and Entrepreneur Workshop (VIEW) 2016.

Fabian and Victor participate in the annual MSR Summer School!

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Two IVUL Ph.D. students (Fabian Caba and Victor Escorcia) participate in the annual MSR Summer School.

Adel Bibi and Matthias Mueller going to ICVSS 2017

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Adel Bibi and Matthias Mueller have been accepted to participate in the ICVSS (International Computer Vision Summer Schoole) 2017 in Italy. Admission into this summer school is quite competitive in the vision community.​​​

CEMSE New Faculty: Mohamed Elhoseiny, Assistant Professor of Computer Science

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Prior to joining the KAUST CEMSE Division earlier this year, Mohamed Elhoseiny received his Ph.D. degree from Rutgers University, New Brunswick in 2016, before spending over two years working as a postdoctoral researcher at Facebook in the company’s AI research wing. Elhoseiny joins the Division as an assistant professor of computer science based in the KAUST Visual Computing Center (VCC). He will also act as the PI of the KAUST Computer Vision, Content AI (Vision-CAIR) Research Group. Outside of his duties at KAUST, he is also acting as an artificial intelligence (AI) research consultant for Baidu Research, Silicon Valley AI Lab.

KAUST Ph.D. student Jinhui Xiong wins best paper award

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KAUST computer science Ph.D. student Jinhui Xiong recently won the best paper award at the 24th International Symposium on Vision, Modeling, and Visualization. The symposium took place from September 30 to October 2, 2019, at the University of Rostock, Germany, and provided the opportunity for researchers to discuss a wide range of topics in computer science, including computer graphics, vision, visualization and visual analytics.

Meet KAUST prospective student: Matheus Vinícius Medeiros de Souza

1 min read ·
Matheus Vinícius Medeiros de Souza is a 24-year-old electrical engineering graduate from the city of Natal, Brazil. Matheus first came to KAUST as a visiting student in 2019 and was instantly impressed by the university’s research facilities.

Meet KAUST Prospective Student: Xinge Yang

1 min read ·
Xinge Yang is a 22-year-old graduate who will join KAUST from the University of Science and Technology of China (USTC). Yang will join the University in the fall of 2020 as an M.S./Ph.D. candidate in the KAUST Computational Imaging Group under the supervision of Professor Wolfgang Heidrich.

KAUST-Stanford neural network paper accepted for presentation at ECCV 2020

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A novel KAUST-Stanford University research paper by Panos Achlioptas (Stanford), Ahmed Abdelreheem (KAUST), Fei Xia (Stanford), Mohamed Elhoseiny (KAUST) and Leonidas Guibas (Stanford) was accepted for presentation at the 16th European Conference on Computer Vision (ECCV 2020).

Meet KAUST prospective student: Kilichbek Haydarov

1 min read ·
Kilichbek Haydarov is a graduate student who will join KAUST from Uzbekistan. He will join KAUST as a M.S./Ph.D. candidate and member of the KAUST Computer Vision, Content AI research group under the supervision of Professor Mohamed Elhoseiny. Kilichbek obtained his bachelor's degree in computer science and engineering from Sejong University, South Korea.

KAUST prospective student: Xingdi Zhang

1 min read ·
Xingdi Zhang is a computer science graduate from the University of Electronic Science and Technology of China (UESTC), China. Xingdi will join KAUST in the fall of 2020 as a M.S./Ph.D. candidate and member of the KAUST High-Performance Visualization Group under the supervision of Professor Markus Hadwiger.

Meet KAUST prospective student: Kai Yi

1 min read ·
Kai Yi is a graduate from Xi'an Jiaotong University (XJTU), China, who will join KAUST in the fall of 2020. Kai will join KAUST as a M.S./Ph.D. candidate and member of the KAUST Computer Vision, Content AI research group under the supervision of Professor Mohamed Elhoseiny. KAUST's high reputation and Kai's interest in computer vision and machine learning prompted him to choose the University to further his academic career.

Yipeng Qin

Postdoctoral Research Fellow, Computer Science
Yipeng Qin was a Postdoctoral Research Fellow working with Professor Peter Wonka at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Currently, he is a Lecturer at the School of Computer Science and Informatics in Cardiff University (CU), United Kingdom. Education and Early Career In 2013, Yipeng Qin graduated with a Bachelor of Engineering in Electronic Engineering from Shanghai Jiao Tong University in China. Later during 2017, he obtained his Ph.D. in Computer Science from the National Centre for Computer Animation, Bournemouth University in

Yidan Zheng

Ph.D. Student, Computer Science
Yidan is a MS/PhD student in Computer Science at King Abdullah University of Science and Technology since August 2022, under the supervision of Prof. Wolfgang Heidrich in Computational Imaging Group. Education and Early Career Yidan obtained her bachelor’s degree in Measurement and Control Technology and Instrument from University of Science and Technology of China in 2020. Research Interest Yidan is interested in developing novel computational imaging systems in both hardware and software.

Yazeed Alharbi

Yazeed Alharbi is a Ph.D. candidate under the supervision of Prof. Peter Wonka at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career In 2015, Yazeed obtained his bachelor degree from Purdue University in the computer graphics and visualization track, with a minor in philosophy. In 2018, he received his master degree from King Abdullah University of Science and Technology (KAUST). He mainly learned about computer vision and the process of publication in that field. Research Interest Currently, Alharbi research is focused

Yahya Bokhari

Postdoctoral Research Fellow, Computer Science
Yahya Bokhari is a Postdoctoral Research Fellow at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST), working with Professor Wolfgang Heidrich on enhancing X-ray images under Smart Health Initiative. Education and Early Career Yahya Bokhari received his bachelor's degree in Medical Technology from King Abdulaziz University, KSA. He holds a Master's degree in Bioinformatics and a Ph.D. degree in Computer Science from Virginia Commonwealth University, USA. Research Interest Dr. Yahya Bokhari is mainly interested in Image Processing, Computer Vision

Somayah Albaradei

Ph.D. Student, Computer Science
Somayah Albaradei is a Ph.D. student at Structural and Functional Bioinformatics Group (SFB) under the supervision of Professor Xin Gao at King Abdullah University of Science and Technology (KAUST). ​Somayah obtained her master's degree in computer science from the University of Manitoba, Canada in 2014. Professional Profile 2014: Lecturer at College of Computing and Information Technology-King Abdulaziz University - Jeddah - Saudi Arabia 2010- 2011: Teacher Assistant at College of Computing and Information Technology-King Abdulaziz University - Jeddah - Saudi Arabia 2009-2010: Collaborator

Shuming Liu

Ph.D. Student, Computer Science
Shuming Liu is a Ph.D. student in the computer science program at the Image and Video Understanding Lab (IVUL), King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Bernard Ghanem.

Shariq Farooq Bhat

M.S. Student, Computer Science
Shariq Farooq Bhat is an MS/PhD student under the supervision of Professor Peter Wonka at King Abdullah University of Science and Technology (KAUST). Education and Early Career Shariq Farooq Bhat obtained his bachelor of technology degree in Electronics & Communication Engineering from the National Institute of Technology Srinagar, India in 2018. During bachelor’s degree, he was an intern at Texas Instruments (TI) Center for Embedded Product Design at NSIT in New Delhi. Then, in 2016, he worked at the Indian Institute of Science (IISc) as a computer vision and deep learning research Intern and

Sara Shaheen

Sara Shaheen was a Ph.D. student in Computer Science under the supervision of Professor Bernard Ghanem at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). She is currently an Assistant Professor in Computer Science at Prince Sultan University (PSU) in Riyadh, Saudi Arabia. Education and Career Sara received her Bachelor degree in Computer Science from Prince Sultan University (PSU) in Riyadh in 2010. She joined the MS/PhD program in KAUST and earned her Master and Ph.D. in Computer Science in 2017. She was a part of Image and Video Understanding

Rameen Abdal

Ph.D. Student, Computer Science
Rameen Abdal is a Master student under the supervision of Professor Peter Wonka at King Abdullah University of Science and Technology (KAUST). He has graduated in 2023. Education and Early Career Rameen Abdal obtained his bachelor of technology degree in Electronics and Communications Engineering from the National Institute of Technology in Srinagar, India in 2018. After that, he joined KAUST for the MS/PhD program in computer science to continue his studies. Before joining KAUST, Rameen was an intern at Texas Instruments (TI) Center for Embedded Product Design at Netaji Subhas University of

Peter Wonka

Professor, Computer Science
Professor Peter Wonka is a computer scientist with a research focus in computer vision, computer graphics, remote sensing, and machine learning.

Qian Wang

Ph.D. Student, Computer Science
Qian Wang is a MS/Ph.D. student at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Wonka in his research group. Education Qian Wang graduated with great distinction from Wuhan University with Bachelor's Degree. Research Interest Research interest of Qian Wang is Computer Vision.

Peihao Zhu

Ph.D. Student, Computer Science
Peihao Zhu is a Computer Science Ph.D. candidate at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Peter Wonka in his research group. He has completed his Ph.D and graduated in 2023. Education and Early Career Peihao Zhu obtained his bachelor of engineering in Automation from Northeastern University in China in 2016. After that, he continued his studies in Computer Science and received his master's degree from King Abdullah University of Science and Technology (KAUST) in Saudi Arabia in 2019. Research Interest

Peng Han

Ph.D. Student, Computer Science
Peng Han is a Ph.D. student in Computer Science program at Machine Intelligence and kNowledge Engineering (MINE) Laboratory under the supervision of Professor Xiangliang Zhang at King Abdullah University of Science and Technology (KAUST). Prior to that, he was a Research Associate in the School of Computer Science and Engineering, Nanyang Technological University (NTU) under the supervision of Prof. Aixin Sun, Singapore. He received his M.E. in Computer Science from Renmin University of China (RUC) at BDAI lab (leaded by Prof. Ji-rong Wen) in 2017. Research Interests ​Peng's research interests

Noor Bafageeh

Noor Bafageeh was a Master student in Computer Science Program under the supervision of Professor Bernard Ghanem at Visual Computing Center (VCC) at King Abdullah university of Science and Technology (KAUST). Education and Career Noor earned her bachelor degree in Computer Science from Effat University in Jeddah in 2016. She joined KAUST and received her master degree in Computer Science in 2018. Research Interest She is interested in computer vision, machine learning, Generative Adversarial Networks (GAN) and deep learning. Education Profile M.S., Computer Science, King Abdullah University of

Neil Smith

Research Scientist, Computer Science
Neil Smith is a Research Scientist in the Computational Imaging Group (VCCIMAGING) working with Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Neil joined University of California in San Diego (UCSD) at the start his academic journey. He obtained his bachelor degree in Anthropology with Concentration in Archaeology in 2001. After that in 2003, he received his master degree in Anthropology. He has a doctoral in Anthropology since 2009. Dr. Smith started his career life as a Geographic Information Systems (GIS) Specialist at
KAUST-CEMSE-CS-Vision-CAIR-Mohamed-Elhoseiny

Mohamed Elhoseiny

Associate Professor, Computer Science
Professor Elhoseiny’s research focuses on developing affective artificial intelligence that understands and generates novel visual content. He has contributed to and led numerous seminal works of affective AI art creation.

Modar Alfadly

Ph.D. Student, Computer Science
Modar Alfadly is a Ph. D. candidate in Image and Video Understanding Lab (IVUL) Group under the supervision of Prof. Bernard Ghanem at KAUST. Education and Early Career Alfadly received his bachelor’s degree in Software Engineering from King Fahd University of Petroleum and Minerals (KFUPM) in 2016. A year later, he obtained his master’s degree in Computer Science and successfully defended his thesis, titled “Analytic Treatment of Deep Neural Networks under Additive Gaussian Noise”. Research Interest Modar Alfadly is focusing on understanding deep neural networks to build more intelligent

Merey Ramazanova

Ph.D. Student, Computer Science
Merey Ramazanova is a Computer Science Ph.D. Student in Image and Video Understanding Lab (IVUL) Group at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Bernard Ghanem. Education and Early Career Merey obtained her bachelor's degree in Computer Science from Nazarbayev University in Kazakhstan in 2018. She also was a Research Intern at Okinawa Institute of Science and Technology (OIST) in Okinawa, Japan in 2018. Research Interest She is focusing on her research on data science, computer vision, and machine learning. Education Profile M.S., Computer

Matheus Vinícius Medeiros de Souza

Ph.D. Student, Computer Science
Matheus Vinícius Medeiros de Souza is an MS/Ph.D. candidate in the KAUST Computational Imaging Group under the supervision of Professor Wolfgang Heidrich. Before joining KAUST, Matheus obtained a bachelor's degree in electrical engineering from the Federal University of Rio Grande do Norte, Brazil. Research Interests Matheus's research interests include Computer Vision and Machine learning.

Masheal Alghamdi

Masheal Alghamdi is a Ph. D. candidate in the Computational Imaging Group (VCCIMAGING) under the supervision of Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Masheal obtained her bachelor degree in Computer Science from King Abdul-Aziz University (KAU) in Jeddah, Saudi Arabia in 2009. After that she joined KAUST to receive her master degree in Computer Science in 2014. Masheal is a researcher in the National Center for Computation Technology and Applied Math (CTAM) at King Abdul-Aziz City for Science and Technology (KACST)

Lama Alssum

Ph.D. Student, Computer Science
Lama Alssum is a Ph.D. student in Computer Science under the supervision of Professor Bernard Ghanem in Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Lama studied Information technology at King Saud University (KSU) in Riyadh and graduated with a Bachelor degree in 2011. After her bachelor degree, she joined KSU as a Teaching Assistant. Later on, she continued her studies and earned her Master degree in Computer Science from the School of Computing Science at the University of Glasgow, United Kingdom. Lama first joined

Kumail Alhamoud

M.S. Student, Computer Science
Kumail Alhamoud is a Computer Science M.S Student at KAUST working under the supervision of Professor Bernard Ghanem at the Image and Video Understanding Lab (IVUL). Before joining KAUST, Kumail obtained his B.S in Electrical and Computer Engineering at Cornell University in 2021. His current research interests include continual learning, learning from limited data under distribution shifts, and AI for Science and Biomedicine. Publications

Kilichbek Haydarov

Ph.D. Student, Computer Science
Kilichbek Haydarov is a M.S./Ph.D. candidate at the KAUST Computer Vision, Content AI research group under the supervision of Professor Mohamed Elhoseiny. Before joining KAUST, Kilichbek obtained a bachelor's degree in computer science and engineering from Sejong University, South Korea. Education and early career Kilichbek Haydarov obtained his bachelor’s degree from Sejong University in 2020 February (Seoul, South Korea). His major was Computer Science and Engineering. During his undergraduate years, he was working in Intelligent Media Lab and focusing on anomaly detection and action

Kai Yi

Kai Yi is a Ph.D. candidate in Optimization and Machine Learning Lab led by Professor Peter Richtarik. He received his Computer Science MS from KAUST in Dec. 2021. Before that, he received B.Eng with honor from the Department of Software Engineering, Xi’an Jiaotong University, in June 2019. He has interned at Tencent AI Lab, CMU, NUS, and SenseTime. Research Interest Kai Yi's current research interests include Machine learning optimization in the large-scale data/model era. Conceptual-level knowledge transfer learning: theories and applications. Specifically, he works on machine learning

Juan C. Pérez

Ph.D. Student, Computer Science
Juan C. Pérez is a Ph.D. Candidate in the computer science program under Professor Bernard Ghanem's supervision at the Image and Video Understanding Lab at King Abdullah University of Science and Technology (KAUST). Before joining KAUST, Juan obtained a master's degree and a bachelor's degree ( cum laude) in biomedical engineering from Universidad de Los Andes, Colombia.

Ivan Skorokhodov

Ph.D. Student, Computer Science
Ivan Skorokhodov is a Ph.D. candidate in the Computer Science Program under the supervision of Professor Peter Wonka at Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). His research interests include such areas of deep learning as generative models, hypernetworks and neural rendering. Ivan Skorokhodov has obtained his Ph.D degree and graduated in March 2023. Education and Early Career Ivan obtained his bachelor's degree in Management from the Moscow Engineering Physics Institute in 2014. He continued his studies there and received his master's degree

Humam Alwassel

Ph.D. Student, Computer Science
Humam Alwassel is a Computer Science Ph.D. candidate in Image and Video Understanding Lab (IVUL) Group under the supervision of Professor Bernard Ghanem at King Abdullah University of Science and Technology (KAUST). Education and Early Career Humam obtained his bachelor degree with double major in Computer Science and Mathematics from Cornell University in New York, USA in 2016. After that, he joined KAUST for the MS/PhD program and received his master degree in Computer Science in 2018. Research Interest He’s currently focused on the development of novel computer vision techniques for video

Habib Slim

Ph.D. Student, Computer Science
Habib Slim is a Computer Science Ph.D. candidate at the Visual Computing Center (VCC)/Vison-CAIR at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Mohamed Elhoseiny Education: M.Sc Data Science, Grenoble Institute of Technology, France, 2021 M.Eng Applied Mathematics and Computer Science, Grenoble Institute of Technology, France, 2021

Gordon Guocheng Qian

Guocheng Qian is currently working towards an MS/Ph.D. degree in the Department of Computer Science at King Abdullah University of Science and Technology (KAUST). He is part of the Image and Video Understanding Lab (IVUL), supervised by Prof. Bernard Ghanem. During his Ph.D. studies, he interned at several leading institutions including Snap Research, Meta Reality Lab, and MEGVII research. His primary research interest lies in 3D vision. Education and Early Career Guocheng obtained his bachelor's degree in Engineering with first-class honors from Xi’an Jiaotong University (XJTU) in China in

Guohao Li

Ph.D. Student, Computer Science
Guohao Li is a Ph. D. candidate in Image and Video Understanding Lab (IVUL) Group under the supervision of Prof. Bernard Ghanem at KAUST. Education and Early Career Guohao Li obtained a Bachelor’s Degree in Communication Engineering from Harbin Institute of Technology in China in 2015. In 2018, he received his Master Degree in Communication and Information System from Chinese Academy of Science in China. Research Interest His primary research interest is in Computer Vision and Deep Learning. Publications Education Profile M.Sc. in Communication and Information System, Chinese Academy of

Caigui Jiang

Research Scientist, Computer Science
Caigui Jiang is a Research Scientist at the Visual Computing Center (VCC) working with Professor Helmut Pottmann at King Abdullah University of Science and Technology (KAUST). Education and Early Career Dr. Jiang obtained a bachelor degree in Electronic Engineering and his masters in Pattern Recognition and Intelligent Systems from Xi'an Jiaotong University (XJTU) in China. After that, he joined KAUST to receive his Ph.D. in Computer Science in 2016. Caigui has a diverse experience in the academic and research field. In 2016, he joined Max Planck Institute for Informatics as a Postdoctoral

Carlos Hinojosa Montero

Postdoctoral Research Fellow, Image and Video Understanding Lab
Education and Early Career o Bachelor of System Engineering and Informatics, University: Universidad Industrial de Santander, 2011-2015. o Master of Science in Computer and System Engineering, University: Universidad Industrial de Santander, 2016-2018. o PhD in Computer Science, University: Universidad Industrial de Santander, 2017-2022.

Boris Radovic

Boris Radovic is a Ph.D. candidate in the computer science program, under the supervision of Professors Marco Canini and Veljko Pejović, and is a member of the Software-defined Advanced Networked and Distributed Systems (SANDS) Lab. He holds a Bachelor of Science (B.Sc.) in Computer Science and a Master of Science (M.Sc.) in Data Science, both obtained from the Faculty of Computer and Information Science at the University of Ljubljana, Slovenia. During his academic journey, Boris gained experience across diverse computer science domains through his long-term collaboration with the Laboratory

Ahmed S. Abdelreheem

Ahmed Abdelreheem is a Computer Science MS. student at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career Ahmed Abdelreheem is a graduate of Cairo University, where he received a B.S. degree in Computer Engineering in 2019. Research Interest Ahmed focuses on Computer Vision and Machine Learning. Education Profile BSc., Computer Engineering, Cairo University, Egypt, 2019
<> computer graphics (42)

Discrete Laplacians for Polygons and Polyhedral

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B4 L5 R5209

Being able to accurately solve PDEs on arbitrary polygonal/polyhedral meshes is a central goal and has been considered for various differential operators over the last years. In this talk I will present a simple approach for computing (piecewise) linear and quadratic basis functions for general polygons and polyhedra, from which discrete operators for gradient, divergence and Laplacian can be derived.

Web-based 3D Visualization

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B9 L2 H2 H2

In this seminar I will present how to create 3D computer graphics and visualization systems for the web, using WebAssmbly and WebGPU language specifications, which are new, bleeding-edge technologies. Previously, accelerated graphics on the web was based on JavaScript libraries, which is still very popular, but they do not offer detailed memory management and code optimization, necessary for systems requiring high memory load and high computational demands. WebAssembly and WebGPU can be compiled from the C++ or Rust code, which also allows the deployment of the same codebase either for web or for the desktop-based applications.

Humboldt Research Award for Prof. Heidrich

1 min read ·
The Alexander von Humboldt Foundation has awarded a Humboldt Research Award to Prof. Wolfgang Heidrich.

Alumni Focus: Ronell Sicat

1 min read ·
As the volume and complexity of data captured around the world continues to grow exponentially, new ways of exploring and visualizing this data are required. Today, society has moved beyond the traditional desktop computer with tools such as augmented and virtual reality (AR/VR) at the forefront of immersive data visualization and analysis.

KAUST Ph.D. student Jinhui Xiong wins best paper award

1 min read ·
KAUST computer science Ph.D. student Jinhui Xiong recently won the best paper award at the 24th International Symposium on Vision, Modeling, and Visualization. The symposium took place from September 30 to October 2, 2019, at the University of Rostock, Germany, and provided the opportunity for researchers to discuss a wide range of topics in computer science, including computer graphics, vision, visualization and visual analytics.

Meet KAUST prospective student: Feng Liang

1 min read ·
Feng Liang is a computer science and engineering graduate who will join KAUST from the Southern University of Science and Technology (SUSTech), China. Feng will join the University this fall as a M.S. candidate and member of the KAUST Nanovisualization Research Group under the supervision of Professor Ivan Viola.

Dr. Ciril Bohak Joins the Visual Computing Center

1 min read ·
We are proudly announcing that Dr. Ciril Bohak has joined VCC as a Postdoctoral researcher of Computer Science in October, 2020. Ciril Bohak is a Postdoctoral Researcher and Teaching Assistant in the Laboratory for Computer Graphics and Multimedia, Faculty of Computer and Information Science, University of Ljubljana, Slovenia. His main research interests are Computer Graphics, Data Visualization, and Game Technology. His current research includes real-time medical and biological volumetric data visualization on the web, visualization of geodetic data (LiDAR and orthophoto) and high-energy

Yousif Aldolaijan

Yousif Aldolaijan was a Computer Science Master student under the supervision of Prof. Ivan Viola at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Yousif received his bachelor’s degree in Computer Science at the University of Southern California (USC), Los Angeles, USA in 2018. He joined KAUST to continue his studies in Computer Science and received his Master's degree in Computer Science from KAUST in 2020. During the summer of 2018, he joined Google as a Software Engineering Intern for three months. He worked on

Yipeng Qin

Postdoctoral Research Fellow, Computer Science
Yipeng Qin was a Postdoctoral Research Fellow working with Professor Peter Wonka at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Currently, he is a Lecturer at the School of Computer Science and Informatics in Cardiff University (CU), United Kingdom. Education and Early Career In 2013, Yipeng Qin graduated with a Bachelor of Engineering in Electronic Engineering from Shanghai Jiao Tong University in China. Later during 2017, he obtained his Ph.D. in Computer Science from the National Centre for Computer Animation, Bournemouth University in
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Xingdi Zhang

Ph.D. Student, Computer Science
Visualization: Riemannian Geometry based observer relative vortex extraction; Computer vision: depth estimation, point cloud understanding. Computer graphics: discrete geometry, graphics engine, etc.

Wolfgang Heidrich

Professor, Computer Science
Professor Heidrich is apinoeer in the development of novel sensing and display technologies by combining computer graphics, machine vision, imaging, inverse methods, optics and perception.

Torsten Hadrich

Ph.D. Student, Computer Science
Torsten Hadrich is a Ph.D. candidate at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Dominik Michels in Computational Sciences Group (CSG). Education and Early Career Torsten Hadrich obtained his bachelor degree in Computer Science and Media from Stuttgart Media University in Germany in 2010. After that, he received his master degree in Information Engineering from the University of Konstanz in Germany in 2014. Torsten started his career life early in 2002. He was an IT Specialist for Application

Suhib Alsisan

Suhib Alsisan was a Master student in Computer Science Program at Visual Computing Center (VCC) at King Abdullah university of Science and Technology (KAUST). Currently, he is a Software Engineer at Facebook, Washington, United States. Education and Career He received his bachelor degree in Computer Engineering from University of Jordan, Jordan in 2009. He joined KAUST and earned his master degree in Computer Science in 2011. Education Profile M.S. Computer Science, King Abdullah university of Science and Technology (KAUST), Saudi Arabia, 2011 B.S., Computer Engineering, University of Jordan

Sara Shaheen

Sara Shaheen was a Ph.D. student in Computer Science under the supervision of Professor Bernard Ghanem at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). She is currently an Assistant Professor in Computer Science at Prince Sultan University (PSU) in Riyadh, Saudi Arabia. Education and Career Sara received her Bachelor degree in Computer Science from Prince Sultan University (PSU) in Riyadh in 2010. She joined the MS/PhD program in KAUST and earned her Master and Ph.D. in Computer Science in 2017. She was a part of Image and Video Understanding

Ronell Sicat

Research Scientist, Computer Science
Ronell Sicat is a Research Scientist in the High Performance Visualization Group working with Professor Markus Hadwiger at King Abdullah University of Science and Technology (KAUST). His research interests are in large-scale data visualization and analysis, immersive analytics, and computer graphics.

Peter Wonka

Professor, Computer Science
Professor Peter Wonka is a computer scientist with a research focus in computer vision, computer graphics, remote sensing, and machine learning.

Peter Rautek

Research Scientist, Computer Science
Peter Rautek is a Research Scientist in the High-Performance Visualization Group working with Professor Markus Hadwiger at King Abdullah University of Science and Technology (KAUST).

Ondrej Strnad

Research Scientist, Computer Science
Ondrej Strnad is a Research Scientist working with Professor Ivan Viola in the Nanovisulization Research Group at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). He joined the group starting in November 2019. Education Profile Ph.D., Informatics, Masaryk University Brno, Czechia, 2014 Mgr. (M.Sc.), Computer Graphics, Masaryk University Brno, Czechia, 2009 B.Sc., Informatics, Masaryk University Brno, Czechia, 2006

Omar Mena Facio

M.S. Student, Computer Science
Omer Mena Facio is a Master student in Computer Science under the supervision of Professor Ivan Viola at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career Omer Mena Facio

Ngan Nguyen

Ph.D. Student, Computer Science
Ngan Nguyen is a Ph.D. candidate in the Computer Science Program under the supervision of Professor Ivan Viola in the Nanovisualization Research Group at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Ngan has a Master's Degree in Computer Science. She worked at Gameloft Company as a Developer. After that, she joined the University of Information Technology, Vietnam National University in Ho Chi Minh City as a Lecturer. Research Interest Ngan is focusing on Computer Graphics, Visualization, and their overlaps into

Mohamed Ibrahim

Postdoctoral Research Fellow, Computer Science
Mohamed Ibrahim is a Postdoctoral Research Fellow in High-Performance Visualization Group (VCCVIS) working with Prof. Markus Hadwiger at King Abdullah University of Science and Technology (KAUST). Education and Early Career He earned his Bachelor’s degree in Computer Engineering from the American University in Cairo (AUC) in 2010. Later on, he obtained a Master’s degree in Computer Science from KAUST in 2012. He graduated from KAUST with a Ph.D. in Computer Science in 2019. Research Interest Mohamed Ibrahim's research is concerned with Large-Scale data visualization. He's especially interested

Modar Alfadly

Ph.D. Student, Computer Science
Modar Alfadly is a Ph. D. candidate in Image and Video Understanding Lab (IVUL) Group under the supervision of Prof. Bernard Ghanem at KAUST. Education and Early Career Alfadly received his bachelor’s degree in Software Engineering from King Fahd University of Petroleum and Minerals (KFUPM) in 2016. A year later, he obtained his master’s degree in Computer Science and successfully defended his thesis, titled “Analytic Treatment of Deep Neural Networks under Additive Gaussian Noise”. Research Interest Modar Alfadly is focusing on understanding deep neural networks to build more intelligent

Michael Birsak

Research Scientist, Computer Science
Michael Birsak is a Research Scientist working with Professor Peter Wonka at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST).

Libo Huang

Ph.D. Student, Computer Science
Libo Huang is a Ph.D. candidate at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Dominik Michels in Computational Sciences Group (CSG). Education and Early Career Libo Huang obtained his bachelor degree in Geophysics from Peking University in China in 2015. After that, he joined KAUST and received his master degree in Earth Science and Engineering in 2016. Education Profile M. Sc., Earth Science and Engineering , King Abdullah University of Science and Technology (KAUST), Saudi Arabia, 2016 B. Sc

Juris Grigs

Ph.D. Student, Computer Science
Juris Grigs is a Computer Science Ph.D. student in the KAUST High-Performance Visualization Group (VCCVis) under the supervision of Professor Markus Hadwiger at King Abdullah University of Science and Technology (KAUST). Education and Early Career Juris obtained his bachelor's degree in ​Physics from the University of Latvia in 2017. He joined KAUST as an Electrical Engineering student with the Integrated Microwaves Packaging Antennas & Circuit Technology (IMPACT) Research Group. Now,he is a Ph.D. student in the VCCVis Research Group. Research Interests Juris's research interests include ​data

Julio Rey Ramirez

Ph.D. Student, Computer Science
Julio Rey Ramirez is a Master student under the supervision of Prof. Wolfgang Heidrich at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career Julio Rey Ramirez was an electronics engineer from National University of Colombia (UNAL). He worked as laboratory assistant in the lighting testing lab (LABE) at UNAL from 2015 to 2018. Julio Rey Ramirez joined KAUST as a visiting student in 2019, and worked in the programming of machine vision cameras and computational microscopy under the supervision of professor Wolfgang Heidrich

Jing Ren

Ph.D. Student, Computer Science
Jing Ren is a Ph. D. candidate under the supervision of Prof. Peter Wonka at King Abdullah University of Science and Technology (KAUST). Education and Early Career Jing Ren obtained a Bachelor’s Degree in Mathematics and Applied Mathematics from Zhejiang University in China. Later on in 2015, she received her M.Sc. degree in Mathematical and Computational Finance from the University of Oxford in United Kingdom. Scientific Interest Jing Ren is mainly interested in her research in Computer Graphics and Geometry Processing. Selected Publications One of the recent publication that Jing Ren co

Ibraheem Alhashim

Ibraheem Alhashim is a Remote Researcher working with Professor Peter Wonka. Education and Early Career Ibraheem Alhashim completed his B.Sc. with honors in Computer Science at the Portland State University in 2008, followed by his M.Sc. in Computer Science in 2011 under the supervision of Prof. Hao Zhang and his Ph.D. in Computer Science in 2016 under the supervision of Prof. Hao Zhang and Prof. Ghassan Hamarneh at Simon Fraser University in Canada. During both his Master and Ph.D., Alhashim was working at the GrUVi Lab at Simon Fraser University in Canada. Dr. Alhashim joined KAUST in 2017

Feng Liang

Feng Liang is a former member of the Nanovisualization Research Group at KAUST lead by Professor Ivan Viola. Before joining KAUST, Feng obtained a bachelor's degree in computer science and engineering from the Southern University of Science and Technology (SUSTech), China. Research Interests Feng's research interests include artificial intelligence and computer graphics.

Eva Bones

Ph.D. Student, Computer Science
Eva Bones is a Ph.D. candidate in the Computer Science Program under the supervision of Professor Ivan Viola in the Nanovisualization Research Group at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Research Interest Eva is focusing on Computer Graphics and Visualization. Academic Advisors Ivan Viola, Professor, Computer Science.

Donggang Jia

Ph.D. Student, Computer Science
Donggang Jia is a Ph.D. student in the Computer Science Program under the supervision of Professor Ivan Viola in the Nanovisualization Research Group at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Donggang has a Master's Degree in Artificial Intelligence from the University of Southampton, United Kingdom. Before that, he received his Bachelor's degree in Computer Science at the Beijing University of Posts and Telecommunications, China. Research Interest Donggang is focusing on computer graphics and visualization

Dawar Khan

Postdoctoral Research Fellow, Computer Science
Dawar Khan has been working as a Postdoctoral researcher with Professor Ivan Viola in the Nanovisualization Research Group, at Visual Computing Center, KAUST - King Abdullah University of Science and Technology, Saudi Arabia, since April 2022. Previously he worked as assistant professor at Interactive Media Design Lab, Nara Institute of Science and Technology (NAIST), Japan and at the University of Haripur, Pakistan. Education and Early Career He received the BS and MS degrees in computer science from the University of Malakand, Pakistan, in 2011 and 2014, respectively, and the PhD degree in

Daoming Liu

Postdoctoral Research Fellow, Computer Science
Daoming Liu is a Postdoctoral Research Fellow working with Professor Helmut Pottmann at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career Daoming Liu obtained his Ph.D. degree from the University of Chinese Academy of Sciences (UCAS). During his Ph.D., he spent one year at TUDelft as a visiting doctoral student. Before joining VCC/KAUST, he worked at ShanghaiTech University as a Research Assistant Professor. Research Interest Daoming Liu’s research was focused on physical simulation, geometric computing and their

Da Li

M.S. Student, Computer Science
Da Li is a Master student in Computer Science under the supervision of Professor Ivan Viola at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST).

Cheng Wang

Ph.D. Student, Computer Science
Cheng Wang received his PhD in the Computer Science Program under the supervision of Professor Helmut Pottmann at the Visual Computing Center (VCC). He is now an Assistant Professor at the University of East Anglia in the United Kingdom Education and Early Career Cheng received his Bachelor's Degree in Automation from Tsinghua University, China in 2016. He joined KAUST to continue his studies and received his Master's Degree in Computer Science in 2018. Research Interest Cheng is mainly interested in Computer Graphics, Geometric Computing, and Machine Learning. Education Profile M.S., Computer

Caigui Jiang

Research Scientist, Computer Science
Caigui Jiang is a Research Scientist at the Visual Computing Center (VCC) working with Professor Helmut Pottmann at King Abdullah University of Science and Technology (KAUST). Education and Early Career Dr. Jiang obtained a bachelor degree in Electronic Engineering and his masters in Pattern Recognition and Intelligent Systems from Xi'an Jiaotong University (XJTU) in China. After that, he joined KAUST to receive his Ph.D. in Computer Science in 2016. Caigui has a diverse experience in the academic and research field. In 2016, he joined Max Planck Institute for Informatics as a Postdoctoral

Bolun Wang

Postdoctoral Research Fellow, Computer Science
Bolun Wang is a Postdoc at the Visual Computing Center (VCC) working with Professor Helmut Pottmann at King Abdullah University of Science and Technology (KAUST). Education and early career 2016.09-2022.01 Ph.D. in fundamental mathematics at Honors College & Key Laboratory of Mathematics, Information and Behavioral, Beihang University. Advisor: Prof. Zhiming Zheng and Xin Jiang 2019.02-2020.09 Visiting Student at Geometric Computing Lab, Courant Institute of Mathematical Sciences, New York University Major in Computer Graphics, Computational Geometry Advisor: Prof. Daniele Panozzo 2012.09-2016

Anna Fruehstueck

Anna Fruehstueck was a Ph. D. candidate under the supervision of Prof. Peter Wonka at King Abdullah University of Science and Technology (KAUST) and successfully defended her PhD in Computer Science on April 17, 2023. Education and Early Career Anna Fruehstueck obtained a Bachelor’s Degree in Media Informatics and Visual Computing from Vienna University of Technology in Austria in 2011. Later on in 2015, she received her M.Sc. degree in Visual Computing from Vienna University of Technology in Austria. During her studies, Anna worked as software developer at SimVis in Vienna, Austria. And in

Amani Ageeli

Ph.D. Student, Computer Science
Amani Ageeli is a Ph.D. student in the Computer Science program under the supervision of Professor Markus Hadwiger at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Amani received her bachelor's degree in Computer Science from Umm Al-Qura University in Makkah, Saudi Arabia in 2016. She joined KAUST to continue her studies and earned a master's degree in Computer Science in 2019. Currently, she's working on directed research with Prof. Markus Hadwiger in the High-Performance Visualization Group. Research Interest Amani

Alexandre A. Kouyoumdjian

Research Scientist, Computer Science
Dr. Alexandre A. Kouyoumdjian has joined the Nanovisualization Research Group as a Research Scientist working with Professor Ivan Viola. His main research interests are Visualization & Computer Graphics, Human-Computer Interaction & Virtual Reality, and Microarchitecture. Dr. Alexandre A. Kouyoumdjian has also worked in the industry as an R&D Consultant for a start-up that develops VR applications meant to ease chronic pain and a graphics programmer for a game studio. Education Profile Ph.D., Computer Science, Université Paris-Saclay,FRANCE, 2018 M.S., Computer Science (Bioinformatics and

Alexandra Irger

M.S. Student, Computer Science
Alexandra Irger is a Master student in Computer Science under the supervision of Professor Ivan Viola at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career Before coming to KAUST, Alexandra graduated from TU Wien with a BSc in Media Informatics and Visual Computing in 2023. Research Interest Alexandra is focusing on Scientific Visualization and Computer Graphics.

Abdalla Gaafar

Postdoctoral Research Fellow, Computer Science
Abdalla Gaafar is a Postdoctoral Research Fellow working with Professor Peter Wonka at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career Abdalla obtained his Ph.D. in Computer Graphics at the University of Konstanz in Germany in 2019. He holds a Master of Science degree in Mobile and Personal Communications from King’s College London, and a Master of Business Administration degree from the University of Khartoum in Sudan. Research Interest Dr. Abdalla is mainly interested in Computer Graphics and Visual Computing areas
<> data mining (42)

Novel Data Mining Methods for Virtual Screening of Biological Active Chemical Compounds by Othman Soufan

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H2 B9

Abstract Drug discovery is a process that takes many years and hundreds of millions of dollars to reveal a con dent conclusion about a specific treatment. Part of this sophisticated process is based on preliminary investigations to suggest a set of chemical compounds as candidate drugs for the treatment. Computational resources have been playing a significant role in this part through a step known as virtual screening. From a data mining perspective, the availability of rich data resources is key in training prediction models. Yet, the difficulties imposed by big expansion in data and its

Contributions to In Silico Genome Annotation

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B3 L5 R5209

Abstract Genome annotation is an important topic since it provides information for the foundation of downstream genomic and biological research. It is considered as a way of summarizing part of existing knowledge about the genomic characteristics of an organism. Annotating different regions of a genome sequence is known as structural annotation while identifying functions of these regions are considered as a functional annotation. In silico approaches can facilitate both tasks that otherwise would be difficult and time-consuming. This study contributes to genome annotation by introducing

Mining Genome-Scale Growth Phenotype Data through Constant-Column Biclustering

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B3 L5 R5209

Growth phenotype profiling of genome-wide gene-deletion strains overstresses conditions can offer a clear picture that the essentiality of genes depends on environmental conditions. In this dissertation, we first demonstrate that detecting such "co-fit" gene groups can be cast as a less well-studied problem in biclustering, i.e., constant-column biclustering. Despite significant advances in biclustering techniques, very few were designed for mining in growth phenotype data.

Novel Computational Methods to Predict Drug–target Interactions Using Graph Mining and Machine Learning Approaches

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B3 L5 R5220

Abstract Computational drug repurposing aims at finding new medical uses for existing drugs. The identification of novel drug-target interactions (DTIs) can be a useful part of such a task. Finding computationally DTIs is a convenient strategy to identify potentially new DTIs at low cost with reasonable accuracy. However, the current DTI prediction methods suffer a high false positive prediction rate. Here, we present a comprehensive review of the recent progress in the field of DTI prediction from data-centric and algorithmic-centric perspectives that can help in constructing novel reliable

Genetic Algorithms for Optimization of Machine-learning Models and their Applications in Bioinformatics

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B3 L5 R5209

Abstract ​​​Machine-learning (ML) techniques have been widely applied to solve different problems in biology. However, biological data are large and complex, which often results in extremely intricate ML models. Frequently, these models may have poor performance or may be computationally unfeasible. This study presents a set of novel computational methods and focuses on the application of genetic algorithms (GAs) for the simplification and optimization of ML models and their applications to biological problems. The dissertation addresses the following three challenges. The first challenge is

Large-scale Text and Data Integration for Cancer Genomics Research

Prof. D, Rebholz-Schuhmann (University Cologne, Germany​)​

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B19 L3 H2

Abstract This talk will give an overview of data and text mining in the life science domain, but will also point out the use of semantic technologies for other domains. Such solutions cover the integration of heterogeneous data sources based on Semantic Web standards (e.g., TCGA, Cosmic, others), the development of smart technologies (e.g., stream processing), graph and literature analytics, and open data initiatives. Application domains are biomedical solutions in cancer genomics and targeted projects together with industrial partners exploiting Semantic Web technologies in general. The

Deep Predictive Models for Mining Electronic Health Records

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B1 L4 R4214

There is an increasing growth in the amount of electronic health records (EHRs) being collected by healthcare facilities. Data mining techniques hold great potential to systematically use such data for identifying not only inefficiencies but also best practices that improve care and reduce costs. However, due to the complexity of EHR data, directly applying traditional machine learning techniques may yield unsatisfactory predictive performance. Recent advances in deep learning-based methods provide unprecedented ability to predict patients’ future health status, but they still suffer from the sparsity issue of EHR data.

On Knowledge Graph and Causality for Recommender Systems

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B1 L4 R4214

Knowledge Graph (KG) is a large-scale semantic network consisting of entities/concepts as well as the semantic relationships among them, which could be considered as a concise version of Semantic Web. Recently KG is emerging as a hot topic of knowledge discovery and management under artificial intelligence, facilitating semantic computing. Causal relation is a reflection of user behaviours with backend intention, which is related another emerging hot topic – recommendation interpretability. This talk will cover the recent research progresses in these two areas and highlight some open research challenges in recommender systems.

Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining

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KAUST

Dynamic programming is an efficient technique to solve optimization problems. It is based on decomposing the initial problem into simpler ones and solving these sub-problems beginning from the simplest ones. A conventional dynamic programming algorithm returns an optimal object from a given set of objects. We developed extensions of dynamic programming which allow us (i) to describe the set of objects under consideration, (ii) to perform a multi-stage optimization of objects relative to different criteria, (iii) to count the number of optimal objects,(iv) to find the set of Pareto optimal points for the bi-criteria optimization problem, and (v) to study the relationships between two criteria.

To Encourage or to Restrict: the Label Dependency in Multi-Label Learning

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KAUST

Multi-label learning addresses the problem that one instance can be associated with multiple labels simultaneously. More or less, these labels are usually dependent on each other in different ways. Understanding and exploiting the Label Dependency (LD) is well-accepted as the key to build high-performance multi-label classifiers, i.e., classifiers having abilities including but not limited to generalizing well on clean data and being robust under evasion attack.

Student life: studying and working at the Computational Bioscience Research Center (KAUST)

1 min read ·
For Ph.D. students Arturo Magana Mora, Haitham Ashoor and Olaa Motwalli, April 2017 was a particularly nerve-wracking month. After years of hard work and sleepless nights, they finally defended their thesis, bringing them one step closer to graduation.

Sarah Alghamdi defends her master's thesis

1 min read ·
Sarah Alghamdi successfully defended her master's thesis on April 10, 2018. Her project, entitled "Ontology Design Patterns for Combining Pathology and Anatomy: Application to Study Ageing and Longevity in Inbred Mouse Strains", was well-received by a panel of professors that included Professor Vladimir Bajic, Associate Professor Xin Gao and Assistant Professor Robert Hoehndorf.

New Book "Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining" of Professor Moshkov and His Former Students is Published by Springer

1 min read ·
A new book - "Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining" by Hassan AbouEisha, Talha Amin, Igor Chikalov, Shahid Hussain, and Mikhail Moshkov - was recently published by Springer.

KAUST prospective student: Xiaochuan Gou

1 min read ·
Xiaochuan Gou, 27, from Dalian, China, will join KAUST in the fall of 2020. He will join KAUST as a Ph.D. candidate and member of the Machine Intelligence and kNowledge Engineering research group under the supervision of Professor Xiangliang Zhang.

Zhuo Yang

Ph.D. Student, Computer Science
Zhuo Yang is a Ph.D. student in Computer Science program at Machine Intelligence and kNowledge Engineering (MINE) Laboratory under the supervision of Professor Xiangliang Zhang at King Abdullah University of Science and Technology (KAUST). Research Interests Zhuo's research interests include machine learning and data mining. Education Profile ​B. E., Computer Science, Nankai University, China, 2014. M. E., Computer Science, Nankai University, China, ​2017.

Xiaochuan Gou

Ph.D. Student, Computer Science
Xiaochuan Gou is a Ph.D. candidate in the KAUST Machine Intelligence and kNowledge Engineering research group (MINE) under the supervision of Professor Xiangliang Zhang. Before joining KAUST, Xiaochuan obtained his master’s degree in Computer Science from National Chiao Tung University and a bachelor’s degree in Spatial Information and Digital Technology from Wuhan University, China, respectively. Research Interests Xiaochuan's research interests include machine learning, data mining, and GIS. He aspires to build new machine learning models to resolve existing problems in public traffic

Vasiliki Kordopati

Vasiliki Kordopati obtained Master degree in Computer Science under the co-supervision of Professor Boon Ooi at Photonics Laboratory and Prof. Xin Gao at Structural and Functional Bioinformatics Group (SFB) from King Abdullah University of Science and Technology (KAUST). Vasiliki is a co-founder of Oæsis, a King Abdullah University of Science and Technology (KAUST) startup spinout, where she is responsible for product development. A computer engineer by training, she received a bachelor's degree in Computer Engineering from the University of Patras, Greece. Research Interest Vasiliki's

Uchenna Akujuobi

Uchenna Akujuobi obtained his Ph.D. degree in computer science under the supervision of Prof. Xiangliang Zhang leading the MINE group at King Abdullah University of Science and Technology (KAUST). Research interests Uchenna's research interests included Machine learning, data mining, Social Networks, Graph mining, Information Retrieval, Text Mining, Big Data, and Deep Networks. Ph.D. dissertation entitled “Learning from Scholarly Attributed Graphs for Scientific Discovery”​. First employment: Research Scientist, Sony AI​, Tokyo, Japan. He is also the creator of Delve, a dataset retrieval, and

Su Wang

Su Wang obtained a master's degree in Computer Science under the supervision of Professor Xiangliang Zhang at Machine Intelligence and kNowledge Engineering (MINE) Laboratory in King Abdullah University of Science and Technology (KAUST). First employment: Didi Chuxing​, Beijing, China, 2016. Research Interests Su's research interests included ​Data mining and machine learning. Education Profile ​​B.Sc., Information Science & Electronic Engineering, Zhejiang University, 2014. M.Sc., Computer Science, King Abdullah University of Science and Technology (KAUST). Awards and Distinctions First Prize

Shichao Pei

Ph.D. Student, Computer Science
Shichao Pei is a Ph.D. student in Computer Science program at Machine Intelligence and kNowledge Engineering (MINE) Laboratory under the supervision of Professor Xiangliang Zhang at King Abdullah University of Science and Technology (KAUST). Research Interests Shichao Pei research interests include machine learning and data mining. Education Profile ​B. E., Software Engineering, Xi'an Jiaotong University​, 2016.

Shuai Zheng

Shuai Zheng obtained his Master's degree in Computer Science under the supervision of Professor Xiangliang Zhang at Machine Intelligence and kNowledge Engineering (MINE) Laboratory at King Abdullah University of Science and Technology (KAUST). First employment: Ph.D. student, University of Texas at Arlington​, 2013 Research Interests Shuai's research interests included Data mining, Cloud computing and High Performance Computing. Selected Publications ​Dan Williams, Shuai Zheng, Xiangliang Zhang, Hani Jamjoom, "TideWatch: Fingerprinting the Cyclicality of Big Data Workloads". Accepted by the

Senay Kafkas

Research Scientist, Bio-Ontology Research Group
Research Interests Senay's research interests include biomedical text/data mining, Natural Language Processing (NLP), and Next Generation Sequencing (NGS) data analysis. Scientific and Professional Membership ​International Society of Computational Biology (ISCB) (2010-present). KAUST Affiliations Bio-Ontology Research Group (BORG).​ Computational Bioscience Research Center (CBRC). Computer, Electrical, and Mathematical Sciences and Engineering (CEMSE). Education Profile Ph.D., Computer Engineering, Eastern Mediterranean University, Cyprus, 2012. M.Sc., Computer Engineering, Eastern

Robert Werfelmann

Robert Werfelmann obtained a Master's Degree in Computer Science program at Machine Intelligence and kNowledge Engineering (MINE) Laboratory under the supervision of Professor Xiangliang Zhang at King Abdullah University of Science and Technology (KAUST). He did his thesis in a task of Natural Language Processing where the goal is to predict the native language of the author of a non-native text. During his studies, he has built and improved skills in several topics including algorithm design and analysis, concurrent computing, computer networking, and artificial intelligence fields such as

Qiannan Zhang

Ph.D. Student, Computer Science
Qiannan Zhang is a Ph.D. student in Computer Science program at Machine Intelligence and Knowledge Engineering (MINE) Laboratory under the supervision of Professor Xiangliang Zhang at King Abdullah University of Science and Technology (KAUST). ​​​​​Research interests Qiannan's research interests include Machine learning and data mining​. Education Profile B. E., Communication Engineering, Tianjin University, China, 2010-2014. M. E., Communication Engineering, Tianjin University, China, 2014-2017. Research Intern, KAUST, 2017.

Othman Soufan

Research Interests I’m a Ph.D. in Computer Science; currently focusing on developing drug discovery and biomedical applications through data mining and graph mining techniques. I excel in analyzing data, extracting unseen useful patterns and achieving a better informative decision-making process. The result of my work can be found in one filed patent and several peer-reviewed publications in international journals ranked top in the respective domain of specialty. I have a strong business background, both academic and empirical, derived from my education and family business. Being transparent

Mohammad Mohiuddin Azad

Ph.D. Student, Computer Science
Research Interests Study of greedy and dynamic programming algorithms for construction of exact and approximate decision trees, rules and reducts for decision tables with many-valued decisions Analysis and design of classifiers based on decision trees, reducts, and decision rule systems Data mining, Machine Learning Selected Publications Mohammad Azad, Igor Chikalov, Shahid Hussain and Mikhail Moshkov. “Restricted multi-pruning of decision trees”. MLDAS (Machine Learning and Data Analytics Symposium) 2016 organized by QCRI (Qatar Computing Research Institute) and Boeing International, Doha

Mikhail Moshkov

Professor Moshkov specializes in various areas of discrete applied mathematics, theoretical computer science and operations research. He is the author or co-author of more than 200 research publications, including 10 books published by Springer.

Maha Thafar

Ph.D. Student, Computer Science
Research Interests I am a computer scientist focusing on developing novel computational methods using artificial intelligence, machine learning, deep learning, and graph mining techniques to solve biomedical problems. I am developing these methods to be applied to large-scale chemical and biomedical data. Currently, I'm working on compound-protein interactions prediction and binding affinity prediction projects. My objective is to empower women in my country with a flourishing academic background that enables building better careers for themselves. Additionally, to create a research culture

Luke Slater

Ph.D. Student, Computer Science
Luke Slater is working as a Research Fellow in the Institute of Cancer and Genomics with an interest in ontology and ontology accessories, and their combination with other interdisciplinary techniques to gain novel and practically useful insights into information.

Lu Yu

Lu Yu obtained his Ph.D. degree in computer science under the supervision of Prof. Xiangliang Zhang leading the MINE group at King Abdullah University of Science and Technology (KAUST). He mainly worked on learning representation of discrete data structure (e.g. graph, sequence, entity) that intersected with graph theory, optimization, adversarial learning. Before joining KAUST, he worked as a Data Mining Engineer at Alibaba Group. Ph.D. dissertation entitled “Exploring Entity Relationship in Pairwise Ranking: Adaptive Sampler and Beyond”. First employment: Senior Algorithm Engineer, Ant Group

Ibrahim Alabdulmohsin

Ph.D. Student, Computer Science
Ibrahim Alabdulmohsin obtained a Ph.D. degree in Computer Science under the co-supervision of Professor Xiangliang Zhang at Machine Intelligence and kNowledge Engineering (MINE) Laboratory and Professor Xin Gao at Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Currently, Ibrahim is working with IT Strategy and Technology Planning Department at Saudi Aramco. Pursued his Ph.D. degree at MINE Lab from 2012 - 2017. Ibrahim obtained his M.Sc. degree in Computer Science from Stanford University. Dissertation Title: Learning via Query

Hongyan Bao

Ph.D. Student, Computer Science
Hongyan Bao is a Ph.D. student at Machine Intelligence and kNowledge Engineering (MINE) Laboratory under the supervision of Professor Xiangliang Zhang at King Abdullah University of Science and Technology (KAUST). Research Interests ​​Hongyan's research interests include Data Mining. Selected Publications ​Zaiqiao Meng, Shangsong Liang, Hongyan Bao, and Xiangliang Zhang. Co-Embedding Attributed Networks. Accepted by The Twelfth International Conference on Web Search and Data Mining (WSDM 2019), February ​11-15, 2019, Melbourne, Australia. (Acceptance rate = 84/511 = 16%). Education Profile B

Guoda Chen

Guoda Chen obtained his M.S. degree in Computer Science under the supervision of Prof. David Keyes (HiCMA Research Group) and Prof. Panagiotis Kalnis (Cloud Research Group) at King Abdullah University of Science and Technology (KAUST). Research Interest Guoda's research interests included Data Management and Data Mining.

Guolei Sun

Guolei Sun obtained his MS degree in the Computer Science program at Machine Intelligence and kNowledge Engineering (MINE) Laboratory under the supervision of Professor Xiangliang Zhang at King Abdullah University of Science and Technology (KAUST). Research interests Guolei's research interests include Machine learning and data mining. Education Profile ​B.E., Biomedical Engineering, Huazhong University of Science & Technology​, 2015​. M.Sc., Computer Science, King Abdullah University of Science and Technology (KAUST).

Fawaz Alsolami

Ph.D. Student, Computer Science
Research Interests Sequential optimization for inhibitory rules relative to length and coverage Analysis and design of classifiers based on inhibitory rules and trees Machine Learning and Data Mining Selected Publications F. Alsolami, T. Amin, M. Moshkov, B. Zielosko: Comparison of heuristics for optimization of association rules. CS&P 2015: 4-11 (2015) F. Alsolami, T. Amin, I. Chikalov, M. Moshkov, B. Zielosko: Dynamic programming approach for construction of association rule systems. CS&P 2015: 12-21 (2015) F. Alsolami, I. Chikalov, M. Moshkov: Comparison of heuristics for inhibitory rule

Ehab Abdelhamid

Ph.D. Student, Computer Science
Ehab Abdelhamid is a Ph.D. student in InfoCloud Research Group (CLOUD) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Panagiotis Kalnis. Research Interests Ehab's research interests include Data mining over very large datasets and Information Retrieval. Currently working on pattern mining in large graphs; The approach currently developed eliminates unnecessary iterations that could not be avoided in previous related work. As a result, improved performance in large graphs. Selected Publications ​Ehab A. Mohamed , Ahmed R.and Samhaa R. , 2009,

Di Wang

Assistant Professor, Computer Science
Professor Wang's research addresses issues and societal concerns arising from machine learning, particularly in the areas of privacy, security, safety, fairness, robustness, interpretability and transparency. He aims to develop provable and practical algorithms to address trustworthiness issues in machine learning.

Chisato Fujii

Chisato Fujii obtained her Master's degree in Computer Science under the supervision of Prof. Xin Gao at Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Research Interests Chisato's research interests included Bioinformatics and Computational Biology. Born in Japan, Chisato is a software engineer at a fashion technology startup in Cambridge. She works on the development of 3-dimensional garment rendering on a customizable body shape unique to the shopper's size. How would you describe your work/activities at KAUST? ''I studied a

Basmah Altaf

Basmah Altaf obtained her Ph.D. degree in Computer Science under the supervision of Prof. Xiangliang Zhang at Machine Intelligence and kNowledge Engineering (MINE) Laboratory in King Abdullah University of Science and Technology (KAUST). Research Interests​​ Basmah's research interests include Recommender systems and Data mining​. Education Profile ​B. E., Computer & Information Systems, N.E.D. University of Engineering and Technology, Karachi, Pakistan, 2010. M. S., Computer Science, KAUST, 2013. Awards and Distinctions ​'Gold Medal' for securing '1st Position' among 140 students in Bachelor

Azza Althagafi

Ph.D. Student, Computer Science
Azza Althagafi is a Ph.D. candidate in the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Robert Hoehndorf. Professional Profile ​2016 to present Graduate Student, KAUST, Thuwal, Saudi Arabia. 2015 - 2016: Teaching Assistant, Taif University, Taif, Saudi Arabia. 2014 – 2015: Volunteer Teaching Assistant, Umm Al-Qura University, Makkah, Saudi Arabia. 2013 – 2016: Assistant Trainer, Mawhiba, The King Abdulaziz and His Companions' Foundation for Giftedness and Creativity, Makkah, Saudi Arabia. Scientific and

Arturo Magana Mora

​Arturo Magana-Mora obtained his Ph.D. in Computer Science at the King Abdullah University of Science and Technology (KAUST) under the supervision of Prof. Vladimir Bajic in 2017 and later joined the National Institute of Advanced Industrial Science and Technology (AIST) in Japan as a postdoctoral research fellow in the Com. Bio Big-Data Open Innovation Lab (CBBD-OIL). Research Interest His research interests include the development of novel machine-learning and data mining techniques to address the complex problems in biology. His research work has resulted in several peer-reviewed

Abdulhakim Qahtan

Abdulhakim Qahtan obtained his Ph.D. degree in Computer Science under the supervision of Professor Xiangliang Zhang at Machine Intelligence and kNowledge Engineering (MINE) Laboratory at King Abdullah University of Science and Technology (KAUST). Dissertation Title: Efficient Estimation of Dynamic Density Function with Applications in Streaming Data. First employment: Postdoctoral researcher at QCRI in Qatar. Research Interests Abdulhakim's research interests included Data mining, Statistics​​​ and Applying statistical models in machine learning. Selected Publications Basma Alharbi, Abdulhakim
<> cybersecurity (37)

Security analysis of Docker-based containerized environments

Operating System (OS) virtualization, also known as container-based virtualization, has gained momentum over the past few years thanks to its lightweight nature and support for agility. However, its compelling features come at the price of a reduced isolation level compared to the traditional host-based virtualization techniques, exposing workloads to various faults, such as container escape. Those faults might be manifested as host OS bugs, container runtime vulnerabilities, and/or poor container deployment choices and profile configuration. The latter aspect is particularly critical as

Monitoring containerized environments for security state error detection

Operating System (OS) virtualization, also known as container-based virtualization, has gained momentum over the past few years thanks to its lightweight nature and support for agility. However, its compelling features come at the price of a reduced isolation level compared to the traditional host-based virtualization techniques, exposing workloads to various threats, such as container escape. In those threats, compromised or rogue containers might exploit existing vulnerabilities or poor container deployment choices to successfully inject security state errors (e.g., breaking out of the

Advanced Breach and Attack Simulation using ML

Cybersecurity is becoming a need more than ever. Organizations need to protect their digital assets and are required to earn certifications to prove the compliance to the regulations and rules. For this, these assets must be assessed to ensure the target security posture and to get certified or pass an audit on yearly basis. This is a daunting and costly task as it often requires a third-party tester that tries to penetrate the system, under agreement. Breach and Attack Simulation is a new method that allows to do this penetration testing in-house, using some automation tools. Some of these

Breaking the Vehicle Over-The-Air Update System

A modern vehicle is composed of around 100 Electronic Control Unit (ECU) connected via several types of networks. An ECU is an embedded device, similar to a RaspberryPI, running an operating system, e.g., Linux-based or real-time OS, on top of which different software and firmware may run, depending on the application. Due to the imperfection of humans, software can have faults and vulnerabilities, which can lead to catastrophic failures that threatens human lives. This makes the manufacturers liable to such failures and thus often caused millions of vehicles recalls for repair. A smart

Adaptive Intrusion Management System for Databases

Muhamad Felemban, Assistant Professor, Computer Engineering Department, KFUPM

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B9 L2 H1

With the growing cyber-security threats to governmental and organizational infrastructures, the need to develop high resilient systems that preserve the security and privacy of data is becoming increasingly important. Although there is a large body of work on security and privacy countermeasures, cyber-attacks still persist. A prominent type of such attacks is intrusion attack that aims at data tampering, which can impair the availability and the integrity of data.

Resilient Computing: when Dependability meets Cybersecurity

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B9 L2 H2 R2325

Computing and communications infrastructures have become commodities that transact huge quantities of data and are pervasively interconnected, inside countries, and worldwide. Modern societies largely depend on them.

Finding the linchpins of the dark web: Understanding and detecting malicious infrastructure

Dr. Sumayah Alrwais, Assistant Professor, King Saud University, Riyadh, KSA

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B9 L2 H1 R2322

In this talk, Sumayah will survey a number of malicious hosting infrastructures for different services and approaches to detecting them. Among them are works on an emerging trend of Bulletproof hosting services reselling infrastructure from lower-end service providers, use of residential proxy as a service to avoid server-side blocking and DNS based hosting infrastructure.

Practical Cryptographic Framework to Manage Security and Privacy for IoT Endpoint devices

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B1 L4 R4214

In this talk, the speaker will provide a high-level introduction to his recent research on IoT endpoint security. Firstly, he will introduce requirements followed by a discussion on cryptographic algorithm implementation. He will mainly focus on an overview of efficient cryptography for IoT endpoints and system privacy issues. Then he will discuss security management approaches, positives, negatives and challenges to resolve, linking to the endpoint device security section with regards to realistic device needs/capabilities.

Multi-layer Red Team vs. Blue Team Methods towards Secure Cyber-Physical Energy Systems

Charalambos (Harrys) Konstantinou, Assistant Professor of Electrical and Computer Engineering with Florida A&M University and Florida State University (FAMU-FSU) College of Engineering

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B9 L2 H1

Election hacking, power grid cyber-attacks, troll farms, fake news, ransomware, and other terms have entered our daily vocabularies and are here to stay. Cybersecurity touches nearly every part of our daily lives. Most importantly, national security and economic vitality rely on a safe, resilient, and stable cyber-space. We rely on cyber-physical systems with hardware devices, software platforms, and network systems to connect, travel, communicate, power our homes, provide health care, run our economy, etc. However, cyber-threats and attacks have grown exponentially over the past years, exposing both corporate and personal data, disrupting critical operations, causing a public health and safety impact, and imposing high costs on the economy. In this talk, we will focus on cyber-physical energy systems (CPES) as the backbone of critical infrastructure, and provide a research perspective and present red team security threats, challenges, and blue team countermeasures. We will discuss recent approaches on developing low-budget targeted cyberattacks against CPES, designing resilient methods against false data, and the need for an accurate assessment environment achieved through the inclusion of hardware-in-the-loop testbeds.

Cybersecurity of Industrial Control Systems - Challenges and Research Directions

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B9 L2 H2 H2.

This Distinguished Lecture is part of the CS Graduate Seminars. Abstract Industrial Control Systems play an important role in monitoring, controlling, and automating critical infrastructure such as water, gas, oil, and electricity. Recent years have seen a number of high-profile cyber attacks on such infrastructure, exemplified by Triton, Colonial Pipeline, and the Ukrainian Power Grid attacks. This naturally begs the question: how should we manage cyber security risks in such infrastructure on which the day-to-day functioning of our society relies? What are the complexities of managing

Towards Cyber Resilient Autonomous Vehicle Ecosystems: Pitfalls and Enablers

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B9 L2 H2 H2

Currently, there is a substantial push towards autonomous vehicles in the market. However, fully autonomous vehicles, using extensive fault-tolerance e.g., in x-by-wire functions, are still not quite safe from an accidental faults perspective. Furthermore, they present an even greater threat surface to combined accidental and malicious faults. This pitfall has been very slowly recognized by car makers. The consequences of such accidents or attacks are likely to be severe, life-threatening included. This talk will discuss this threat surface in an analysis including the whole ecosystem.

Security, Privacy, and Human

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B4 L5 R5209

Security and privacy systems are often composed of complex components and details. However, users’ experience shouldn’t be as complex. In this seminar, Eman will discuss the human factor in the security and privacy chain. While human privacy perceptions and behaviors have been investigated in Western societies, little is known about these issues in non-Western societies.

Scheduling tasks in energy harvesting IoT

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B4 L5 R5209

Internet of Things (IoT) applications can exploit energy harvesting systems to guarantee virtually uninterrupted operations. However, the use of energy harvesting poses issues concerning the optimization of the utility of the application while guaranteeing energy neutrality of the devices.

Memory Forensics 2.0

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B9 L2 H2 H2

The risk of security breaches is now higher than ever, and attackers routinely break into corporate networks, government services, and even critical infrastructures. As a result, it is not a matter of `if' a system will be compromised, but only a matter of `when' -- thus making the way we handle computer incidents and investigations of paramount importance.

The SCION Research and Education Network

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B9 L2 H2 H2

Imagining a new Internet architecture enables us to explore new networking concepts without the constraints imposed by the current Infrastructure. In this presentation, we invite you to join us on our 14-year-long expedition of creating the SCION next-generation secure Internet architecture.

Security and privacy in centralized and decentralized machine learning

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B4 L5 R5209

Machine learning (ML) is vulnerable to security and privacy attacks. Whereas security attacks aim at preventing model convergence or forcing convergence to wrong models, privacy attacks attempt to disclose the data used to train the model.

RC3 Advisory Board Distinguished Lectures Workshop

RC3 Advisory Board

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B5 L5 R5220

Machine learning (ML) has witnessed remarkable advancements in recent years, demonstrating its effectiveness in a wide array of applications, including intrusion detection systems (IDS). However, when operating in adversarial environments, ML-based systems are susceptible to a range of attacks.

Towards Transparency of IoT Device Presence

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B9 L2 H2 H2

As many types of IoT devices worm their way into numerous settings and many aspects of our daily lives, awareness of their presence and functionality becomes a source of major concern. Hidden IoT devices can snoop (via sensing) on nearby unsuspecting users, and impact the environment where unaware users are present, via actuation.

Data, AI and Cybersecurity: Synergies and Interconnection

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B9 L2 H2 H2

This Dean's Distinguished Lecture is part of the ECE Graduate Seminar. Modern Artificial Intelligence (AI) technologies, led by deep learning, have gained unprecedented momentum over the past decade.

Securing Cyber-Physical and IoT Systems in Smart Living Environments

Prof. Sajal K. Das is a Curators’ Distinguished Professor of Computer Science, and Daniel St. Clair Endowed Chair, Missouri University of Science and Technology, USA.

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B4 L5 R5220

Our daily lives are becoming increasingly dependent on smart cyber-physical infrastructures, such as smart homes and cities, smart grid, smart transportation, smart healthcare, smart agriculture, and so on.

Dependability assessment work at City, University of London and possible synergies with RC3 at KAUST

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B4 L5 R5209

Abstract The talk will give an overview of research at the Department of Computer Science at the City University of London, focusing on the past and current dependability work at the Centre for Software Reliability. The talk will conclude with an overview of related research that is continuing or planned to start at City in 2024 and possible synergies and opportunities for collaboration with RC3 at KAUST. Brief Biography Ilir Gashi holds PhD (2007) and BEng (Honours) (2003) degrees in Software Reliability and Computing, respectively, from the City, University of London, where he is currently a

ML-based Network Intrusion Detection Systems: Resilience threats and a mitigation approach

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B4 L5 R5220

With the ever-increasing amount of cyberthreats out there, securing IT and OT infrastructures against these threats has become not only desirable, but fundamental. Network Intrusion Detection Systems (NIDS) are key assets for system protection, providing early alerts of network attacks. An important class of NIDS are those based on ML techniques, around which a substantial amount of research is being done these days. Unfortunately, being ML-based, these NIDS can be targeted by adversarial evasion attacks (AEA), which malicious parties try to exploit to perform network attacks without being detected.

KAUST SENTRY team wins 2021 Cybersecurity Games & Conference Embedded Security Challenge

1 min read ·
The SENTRY Lab team wins the 18th annual CSAW Cybersecurity Games & Conference’s (CSAW '21) Embedded Security Challenge (ESC).

Blocking microgrid cyberattacks to keep the power flowing

1 min read ·
Power grids have become far more complex in recent decades due to energy demands, environmental regulations and small-scale renewable energy systems that turn businesses and individuals into combined consumer-producers. One way to ensure that power supplies remain resilient is to create small groups of sources and loads called microgrids. Microgrids can operate independently of the main grid when required, such as supporting hospitals during natural disasters, for example. As microgrids grow more complex, they require sophisticated computer networks to coordinate, control and distribute

Simple hardware to defend microgrid attacks

1 min read ·
An inexpensive piece of hardware integrated with solar panel controllers can protect isolated power networks from cyberattacks.

Xiaojie Zhu

Xiaojie Zhu is a staff research scientist at KAUST. Prior to that, he was an assistant professor of cybersecurity in the Department of Computer Science and Information Technology, College of Engineering, Abu Dhabi University. He received his Ph.D. from the Department of Computer Science, University of Oslo, Norway. Xiaojie's research interests are data security and privacy, software security, decentralized trust and infrastructure, and applied cryptography. He has published in prestigious journals such as IEEE TDSC, IEEE TPDS, JNCA, and JOS. One of his articles was included in the 21st annual

Roberto Di Pietro

Professor, Computer Science
Professor Di Pietro is a cybersecurity expert who excels in building and leading research and innovation labs in the private sector, government and academia. He works on critical infrastructure protection, adopting an artificial intelligence-driven approach in collaboration with major stakeholders.

Michał Forystek

M.S. Student, Computer Science
Computer Science Master's student at the Secure Next Generation Resilient Systems Lab (SENTRY) under the supervision of Professor Charalambos Konstantinou.

Marc Dacier

Professor, Computer Science
Leveraging 20+ years of international experience, Professor Dacier leads the charge in uncovering internet vulnerabilities and developing robust defenses, building a more secure digital future for all.

Mahmoud Attia

M.S. Student, Computer Science
Mahmoud Attia is currently an MS/PHD student working with the SeRBER research group led by Prof. Marc Dacier. His research interests are in network security, within the areas of IT and OT security. He previously joined KAUST as a visiting student in the summer of 2023.

Ines Pinto Gouveia

Postdoctoral Research Fellow, Cyber Resilience Research Group
Working towards building safe, secure and resilient systems.

Ilies Benhabbour

Ilies is working with professor Marc Dacier on the following subject: Fingerprinting of intra network semi active components in end-to-end communications. That is to say that we want to be able to identify components which may exist between two devices. It can be either something normal like a firewall or a proxy, but it can also be something malicious like a man in the middle attack. The project's goal will thus be to identify how we can do so, and what kind of experiments can be done over the packets to see if these semi active components exist. Another aim would be to create some code that

Hugo Bertin

Hugo is a Visiting Student from the University of Rennes, France. He is interested in the network and system aspects inherent in distributed systems, which often involve a trade-off between security and performance. He joined the SeRBER research group to work on EGaming Security under the supervision of Professor Marc Dacier. The research project aims to investigate the security aspects leveraged by the gaming industry, which experienced unprecedented growth, especially with the surge of Esports and is expected to continue to shape tomorrow's virtual worlds. Massively Multiplayer Online Games

Erick Rodrigues E Silva

Education Erick pursued his education in Control and Automation Engineering at CEFET-RJ, where he was awarded a Motion of Praise for his victory in a national robotics championship. He authored a thesis titled "Platform for Simulation and Generation of Synthetic Data for Autonomous Inspections by UAVS in Solar Plants." Experience During the thesis composition, Erick made a significant contribution to the paper "Virtual Reality Simulation of Autonomous Solar Plant Inspections with Unmanned Aerial Systems." This paper was published at ICUAS, a conference held in Dubrovnik, Croatia in 2021. After

Charalambos (Harrys) Konstantinou

Professor Konstantinou is an expert in critical infrastructure security and resilience, focusing on smart grid technology, renewable energy integration and real-time simulation.

Alya AlShaikh

Alya AlShaikh is currently an undergraduate student at Alfaisal University in Riyadh, KSA. She is pursuing a Software Engineering BSc specializing in Cyber Security and Machine Learning. Alya joined the Secure Next Generation Resilient Systems Lab (SENTRY) led by Professor Konstantinou as an Accelerated Bachelor’s and Master's (ABM) student. Her research interests include applying machine learning techniques to enhance cyber defense strategies.

Ali Shoker

Senior Research Scientist, Computer Science
I am a Senior Staff Scientist, working on Cyber Security and Resilience. My research is focused on studying and using Byzantine and Intrusion Fault tolerance, Hardware (FPGA), Artificial Intelligence, and Blockchain, for critical applications like Autonomous Vehicles, Small Satellite constellations, SOC, and Fintech. I love and plan to build practical things that manifest in the society.
<> High Performance Computing (29) <> optimization (29)

Ahmed Youssef Ragab Radwan

Visiting Student (former), Information Science Lab
Visiting student at the Information Science Lab, in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST)

KAUST Research Workshop on Optimization and Big Data

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B19 L3 H2

The age of "big data" is here: data of unprecedented sizes is becoming ubiquitous, which brings new challenges and new opportunities. With this comes the need to solve optimization problems of unprecedented sizes.

Everything you always wanted to know about single machine large-scale graph analytics

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B9 L2 H1

Abstract: Graphs are a natural way to capture relations among objects or people. This has led to graph processing systems being used in a wide variety of fields, ranging from biology to social networks, and a large number of such systems have been described in the recent literature. The focus of our recent work is on single machine graph processing systems where the graph is processed from main memory or external storage. We perform a systematic comparison of various techniques proposed to speed up in-memory multicore graph processing. In addition, we take an end- to-end view of execution time

Use Mathematics and get a SPARQL Query Engine (almost) for Free! It’s MAGiQ!

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B9 L2 H1 R2322

Existing RDF engines are designed for specific hardware architectures; porting to a different architecture (e.g., GPUs) entails enormous implementation effort. We explore sparse matrix algebra as an alternative for designing a portable, scalable and efficient RDF engine.

Langevin Monte Carlo as an optimization algorithm

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B9 L2 H1 R2322

Adil Salim is mainly interested in stochastic approximation, optimization, and machine learning. He is currently a Postdoctoral Research Fellow working with Professor Peter Richtarik at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST).

Space-Time Tomographic Reconstruction of Deforming Objects

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B1 L2 R2202

In this thesis, a variety of applications in computer vision and graphics of inverse problems using tomographic imaging modalities will be presented: (i) The first application focuses on the CT reconstruction with a specific emphasis on recovering thin 1D and 2D manifolds embedded in 3D volumes. (ii) The second application is about space-time tomography (iii) Base on the second application, the third one is aiming to improve the tomographic reconstruction of time-varying geometries undergoing faster, non-periodic deformations, by a warp-and-project strategy. Finally, with a physically plausible divergence-free prior for motion estimation, as well as a novel view synthesis technique, we present applications to dynamic fluid imaging which further demonstrates the flexibility of our optimization frameworks

Optimal Algorithms for Affinely Constrained, Distributed, Decentralized, Minimax, and High-Order Optimization Problems

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B5 R5220

In this thesis, we discuss a few fundamental and well-studied optimization problem classes: decentralized distributed optimization (Chapters 2 to 4), distributed optimization under similarity (Chapter 5), affinely constrained optimization (Chapter 6), minimax optimization (Chapter 7), and high-order optimization (Chapter 8). For each problem class, we develop the first provably optimal algorithm: the complexity of such an algorithm cannot be improved for the problem class given. The proposed algorithms show state-of-the-art performance in practical applications, which makes them highly attractive for potential generalizations and extensions in the future.

Aerial Path Planning for Urban Scene Reconstruction: A Continuous Optimization Method and Benchmark

1 min read ·
Small unmanned aerial vehicles (UAVs) are ideal capturing devices for high-resolution urban 3D reconstructions using multi-view stereo. Nevertheless, practical considerations such as safety usually mean that access to the scan target is often only available for a short amount of time, especially in urban environments. It, therefore, becomes crucial to perform both view and path planning to minimize flight time while ensuring complete and accurate reconstructions. In this work, we address the challenge of automatic view and path planning for UAV-based aerial imaging with the goal of urban

Adel is invited to give a talk in EECVC on "Optimization meets Deep Learning"

1 min read ·
​Adel is invited to give a talk in EECVC on "Optimization meets Deep Learning"​. It is a 45 minutes talk scheduled on the 6th of July 2019.

Accelerating the grapevine effect

1 min read ·
By looking at classical gossip algorithms from a novel perspective, KAUST Professor Peter Richtarik has found a way to significantly speed up gossip-based information sharing, and in the process, he discovered new applications for this efficient mathematical approach. Gossip involves the sharing of information between individuals in a network and can be applied mathematically in both human social networks and data networks, such as distributed sensors. “A network is a collection of nodes, each connected to other nodes via links,” explains Richtarik. “In social networks, for instance

KAUST Professor Peter Richtárik wins Distinguished Speaker Award

1 min read ·
Peter Richtárik, KAUST professor of computer science, recently received a Distinguished Speaker Award at the Sixth International Conference on Continuous Optimization (ICCOPT 2019) held in Berlin from August 3 to 8. ICCOPT 2019 was organized by the Mathematical Optimization Society and was hosted this year by the Weierstrass Institute for Applied Analysis and Stochastics.

Meet KAUST Prospective Student: Igor Sokolov

1 min read ·
Igor Sokolov, 23, is an applied mathematics and physics graduate who will join KAUST from the Moscow Institute of Physics and Technology, Russia. Igor first came to KAUST as a visiting student in 2019 and was highly impressed by the University's living and working facilities. It was this first impression that prompted him to pursue an M.S. at KAUST. Sokolov will join KAUST in the Fall of 2020 as an M.S. candidate under the supervision of Professor Peter Richtarik.

Tariq Alturkestani

Ph.D. Student, Computer Science
Tariq is a PhD student in Computer Science working under the supervision of Professor David E. Keyes. He is working on developing a library that enables users to overlap I/O and compute. Tariq holds a Bachelors degree from Penn State University and a Master from KAUST. Education Profile MS, Computer Science, King Abdullah University of Science and Technology (KAUST), 2014. BS, Computer Science, Penn State University, USA, 2013.

Ramzi Idoughi

Research Scientist, Computer Science
Ramzi Idoughi is a Research Scientist in the Computational Imaging Group (VCCIMAGING) working with Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Ramzi obtained his engineering degree in Space System and Image Processing from National Higher French Institute of Aeronautics and Space (ISAE/SUPAERO) in France in 2011. Later, he received his master degree in Signal, Image, Acoustics and Optimization (SIAO) from Higher Educational Institution in Toulouse (ENSEEIHT) in France in 2011. After that in 2015, he received his doctoral

Peter Richtarik

Professor, Computer Science
Professor Richtárik specializes in big data optimization and machine learning. He is known for his work on randomized coordinate descent algorithms, stochastic gradient descent and federated learning.

Laurent Condat

I am a senior researcher working in optimization, in the group of Prof. Peter Richtárik.

Konstantin Mishchenko

Ph.D. Student, Computer Science
Konstantin Mishchenko is a Ph.D. candidate at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Richtarik in his research group. Education and Early Career Konstantin Mishchenko obtained his bachelor degree in Computer Science and Physics from Moscow Institute of Physics and Technology in 2016. After that in 2017, he received his master degree in Machine Learning MASH from Université Paris Dauphine in Paris, France. Konstantin had many internships during his studies. The most recent was with Amazon in

Kai Yi

Kai Yi is a Ph.D. candidate in Optimization and Machine Learning Lab led by Professor Peter Richtarik. He received his Computer Science MS from KAUST in Dec. 2021. Before that, he received B.Eng with honor from the Department of Software Engineering, Xi’an Jiaotong University, in June 2019. He has interned at Tencent AI Lab, CMU, NUS, and SenseTime. Research Interest Kai Yi's current research interests include Machine learning optimization in the large-scale data/model era. Conceptual-level knowledge transfer learning: theories and applications. Specifically, he works on machine learning

Guangming Zang

Research Engineer, Computer Science
Guangming Zang is a research engineer in the Computational Imaging Group (VCCIMAGING) under the supervision of Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Guangming obtained his bachelor degree in Software Engineering from China University of Geosciences in Wuhan, China in 2013. He received his master degree in Computer Science from University of Chinese Academy of Sciences in China. Before joining KAUST, Guangming was a research assistant (2013 – 2014) at The National Space Science Center at Chinese Academy of Sciences

Francesco Orabona

Associate Professor, Computer Science
Achieving "parameter-free" machine learning is the primary focus of Professor Francesco Orabona. He is particularly interested in designing superior algorithms to train deep learning models.

El Houcine Bergou

Postdoctoral Research Fellow, ‌InfoCloud
As of January 2019, El Houcine Bergou is a Postdoctoral Research Fellow at Extreme Computing Research Center (ECRC) in King Abdullah University of Science and Technology (KAUST), working with Professor Panagiotis Kalnis in his research group. Prior to this, he spent a year as a Postdoctoral Research Fellow in the Optimization and Machine Learning group of Professor Peter Richtárik. Education and Early Career Dr. Bergou has a PhD degree in numerical methods for least squares problems with application to data assimilation from the National Institute of Posts and Telecommunications (INPT) in

Eric Feron

With 31 years of teaching and research experience, Professor Feron’s work utilizes elementary concepts of control systems, optimization and computer science to address critical issues in modern aerospace engineering and robotics.

Elnur Gasanov

Elnur Gasanov is a master student at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Richtárik in his research group. Education and Early Career Elnur Gasanov obtained his bachelor degree in Applied Mathematics and Physics from Moscow Institute of Physics and Technology (MIPT) in Moscow, Russia in 2018. Research Interest His research interests are in optimization and machine learning. Education Profile ​B.Sc., Applied Mathematics and Physics, Moscow Institute of Physics and Technology (State

Egor Shulgin

Ph.D. Student, Computer Science
Egor Shulgin is a CS student in the MS/Ph.D. program under the supervision of Professor Peter Richtarik at the Visual Computing Center (VCC) at the King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Education and Early Career Egor obtained his bachelor's degree in Applied Mathematics, Physics, and Computer Science from the Moscow Institute of Physics and Technology (MIPT) in 2019. He first came to KAUST for a research visit in 2019. Later, he joined Prof. Peter Richtarik's group as a MS/Ph.D. student. In 2021 Egor did a research internship with Distributed AI team at the

Dmitry Kovalev

Ph.D. Student, Computer Science
Education Dmitry obtained a B.S. degree from Moscow Institute of Physics and Technology, Moscow, Russia, in 2018. He is an M.S. in the Computer Science program at the King Abdullah University of Science and Technology as of Fall 2018. Publications Dmitry Kovalev, Samuel Horvath and Peter Richtárik. Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop, arXiv preprint arXiv:1901.08689, 2019. Dmitry Kovalev, Peter Richtárik, Eduard Gorbunov, Elnur Gasanov. Stochastic Spectral and Conjugate Descent Methods . Advances in Neural Information Processing

Biao Zhang

Ph.D. Student, Computer Science
Biao Zhang is a Computer Science Ph.D. candidate at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Peter Wonka in his research group. Education and Early Career Biao Zhang obtained his bachelor degree in Mathematics and master degree in Applied Mathematics from Xi’an Jiaotong University in China. Research Interest Biao Zhang is mainly interested in machine learning and computer vision. Education Profile M.S., Applied Mathematics, Xi’an Jiaotong University, China B.S., Mathematics, Xi’an Jiaotong University

Alyazeed Basyoni

M.S. Student, Computer Science
Alyazeed Basyoni is a CS Master Student under the supervision of Professor Peter Richtarik at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career Alyazeed joined Carnegie Mellon University in Pittsburgh, Pennsylvania and earned his bachelor degree in Computer Science with minor in Mathematics in 2018. Education Profile B.S., Computer Science, Carnegie Mellon University, USA, 2018

Ali H. AlNasser

M.S. Student, Computer Science
Ali AlNasser is a Masters student in Computer Science. His research interests are scaling deep learning algorithms available now to be used on multicore computers by leveraging the high parallelism in Deep learning. Why KAUST? The abundance of opportunities available in KAUST. Education Profile B.S, Electrical & Computer Engineering, University of Colorado at Boulder. B.S, Applied Mathematics, University of Colorado at Boulder.

Adil Salim

Postdoctoral Research Fellow, Computer Science
Adil Salim is a Postdoctoral Research Fellow working with Professor Peter Richtarik at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Dr. Salim earned his Master degrees in Data Science and Statistical Engineering from ENSAE ParisTech and in Probability Theory and Stochastic Modeling from Paris XI University in France in 2015. He received his Ph.D. in Machine Learning from Télécom ParisTech in France in 2018. Before joining KAUST, Adil worked in many positions starting from an Auditor for banks and insurance companies
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KAUST Cell Visualization Summit 2019

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B9 H2 R2325

You are invited to attend the KAUST Cell Visualization Summit 2019 organized by the Nanovisualization group from the Visual Computing Center (VCC). The summit will focus on the technological advancement that can lead to an in-silico virtual and visual representation by integrating current knowledge into a unified structural and dynamic model of a living cell. Each keynote speaker will relate to one of the technological aspects (modeling, visualization, and interaction). Keynote speakers Arthur Olson Graham Johnson Anders Ynnerman For more information about the summit, please click here. If you

Optimal Reference Frames, Scaling and Features in Visualization

Prof. Holger Theisel, Visual Computing, Magdeburg University

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B9 L2 H1 R2322

In Visualization, the success or failure of an analysis often depends on the choice of some subtle parameters or design choices. While simple heuristics are often sufficient, in some cases they make the analysis miserably fail. We present three approaches in visualization where a careful choice of optimal parameters results in completely new algorithms: 1) the choice of a reference frame for finding objective vortices in flow visualization, 2) the choice of a scaling of high-dimensional data sets for finding linear projections to 2D in information visualization, and 3) the choice of a feature definition along with numerical extraction methods for visualizing recirculation phenomena in flows.

Enabling Accurate and Efficient Simulation in Visual Computing - Public Colloquium

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KAUST

The overarching goal of Prof. Michels' Computational Sciences Group within KAUST's Visual Computing Center is enabling accurate and efficient simulations for applications in Scientific and Visual Computing. Towards this goal, the group develops new principled computational methods based on solid theoretical foundations. This talk covers a selection of previous and current work presenting a broad spectrum of research highlights ranging from simulating stiff phenomena such as the dynamics of fibers and textiles, over liquids containing magnetic particles, to the development of complex ecosystems and weather phenomena. Moreover, connection points to the growing field of machine learning are addressed and an outlook is provided with respect to selected technology transfer activities.

Masterclass on Visual Computing

Dominik Michels, Helmut Pottmann, Ivan Viola, Peter Wonka, Soeren Pirk, Wolfgang Heidrich

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KAUST

Visual Computing has become a key enabling technology for a diverse set of applications spanning scientific discovery, digital services, medicine, robotics, consumer electronics, and entertainment, to name just a few. The research community tackles problems in this vast space by drawing from expertise in multiple disciplines, including Computer Science, Electrical Engineering, and Mathematics. The KAUST Masterclass on Visual Computing highlights a selection of cutting-edge academic research within this field by comprising a series of talks focusing on different topics ranging from Computational Architecture and Fabrication, Deep Optics, and Generative Modeling, to Nanovisualization, Physics-based simulation, and Representation Learning.

Workshop on Applied Geometry and Visual Computing

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B4 L5 R5209

The “KAUST Workshop on Applied Geometry and Visual Computing” brings together leading scientists from Europe and the United States, presenting their latest results in - Applied and Discrete Differential Geometry - Geometry Processing - Computational Fabrication The talks are related to various problems in Applied Mathematics in general and to further areas of Visual Computing such as Computer Graphics, Physical Simulation and Scientific Visualization. The workshop provides a great opportunity to learn about latest developments and to discuss ongoing work with top researchers in the field.

Physics-based Simulation in Visual Computing

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B9 L2 H2 H2

This talk covers a selection of previous and current work presenting a broad spectrum of research highlights ranging from simulating stiff phenomena such as the dynamics of fibers and textiles, over liquids containing magnetic particles, to the development of complex ecosystems and weather phenomena. Moreover, connection points to the growing field of machine learning are addressed and an outlook is provided with respect to selected technology transfer activities.

Peter Wonka's new query tool, called a relation-augmented image descriptor will change forever querying images online.

1 min read ·
Today, querying the massive amounts of images available in online databases such as Instagram can be a time-consuming experience. Researchers from the King Abdullah University of Science and Technology (KAUST) and the University College London, have developed a new tool that generates image queries based on a geometric description of objects in spatial relationships with potential applications in computer graphics, computer vision and automated object classification.

Dominik L. Michels receives the first Procter & Gamble Faculty Award

1 min read ·
Dominik L. Michels, Computer Science and Mathematics faculty in the Computer, Electrical and Mathematical Science and Engineering Division (CEMSE) and head of the Computational Sciences Group within the Visual Computing Center (VCC), recently received the first Procter & Gamble (P&G) Faculty Award.

Polymers offer a better view

1 min read ·
A window opens for analyzing the distribution of small molecules in biological and medical tissue samples.

Teaching algorithms to see

1 min read ·
Four hundred hours of video are uploaded to YouTube every minute. Other than using text to search the title, description or tags associated with a video, video content search is limited. In addition to helping users find content more quickly and accurately, the ability to search video content is of paramount importance to video platforms and advertisers. Last year, YouTube was embroiled in controversy after rolling video ads for Coca-Cola and Amazon, among others, were shown before racist and extremist content. Advertising content is a major revenue stream for video platforms like YouTube

Nanovisualization Research Group is hiring

1 min read ·
Nanovisualization Research Group is hiring scientific staff on the levels of Research Scientist, Postdoctoral Fellow, and Graduate Student. Follow the links to apply for the respective position: Research Scientist position Postdoctoral Fellow Graduate Student

Scanning in the Fourth Dimension

1 min read ·
Three-dimensional (3D) computed tomography is a widely used technology that visualizes an object's external and internal structure by assembling a series of two-dimensional images taken sequentially across or around it. However, as anyone who has had a medical magnetic resonance imaging scan will recall, this type of 3D reconstruction requires the subject to be motionless throughout the capture process, which can take minutes. Capturing a 3D structure that changes or deforms over time is much more difficult, and existing approaches often yield reconstructions marred by image artifacts and

Professor Ivan Viola Joins the Visual Computing Center

1 min read ·
We are proudly announcing that Professor Ivan Viola has joined VCC as an Associate Professor of Computer Science in November, 2018. He is establishing the Nano-visualization Research Group as a part of the Visual Computing Center. Prof. Viola has a master and Ph.D. from TU Wien in Austria. He has received a series of honors and recognition for his contribution to computing visualization such as the Austrian Computer Graphics Award 2016 for the Best Technical Solution, and the 1st Place Eurographics Dirk Bartz Prize for Visual Computing in Medicine 2013. Prof. Viola was attracted to KAUST for

Mastering a prickly problem in ferrofluids

1 min read ·
Computer simulation accurately captures the beguiling motion of a liquid magnetic material.

Making the invisible, visible

1 min read ·
As bioscience and medicine progress, we are now able to live with diseases, which were once a death sentence. And we are rapidly discovering new interactions at the microscopic level that give us insight into the engines of life. However, as research gets more complicated, the layperson gets left further and further behind. Even those who trust the work of scientists can feel overwhelmed when attempting to read the latest research.

Peering under the hood of SARS-CoV-Two

1 min read ·
Microscope and protein data are incorporated into an easy-to-use-and-update tool that can model an organism’s 3D appearance.

Ferrofluid surface simulations go more than skin deep

1 min read ·
Computer models efficiently and accurately simulate the magnetic responses of ferrofluids by considering only the fluid’s surface.

First 3D Visualizations of SARS-CoV-2 Cryo-EM Tomography Data

1 min read ·
Together with our partners from Tsinghua University and Nanographics, our group has succeeded in depicting the first 3D visualizations of Cryo-EM microscopy tomography directly from the data. We are advancing the technology to allow for clear, noise-free visualization of this challenging data modality. Once we succeed with that our next challenge is to integrate such 3D visualization into the structural modeling process. Stay tuned!

Cloud simulations get a dose of realism

1 min read ·
A focus on the fundamental physics of cloud formation leads to highly realistic simulations of different types of clouds.

Wildfire simulation heats up

1 min read ·
Computational models capture the capricious behavior of forest fires.

Harnessing the particle-tracking power of the algorithm

1 min read ·
A simple camera system paired with a sophisticated image-processing algorithm can achieve faster and more accurate reconstructions of particle flow.

Dominik L. Michels appointed Associate Professor of Computer Science and Applied Mathematics

1 min read ·
KAUST faculty member Dominik L. Michels has recently been promoted to associate professor of computer science and applied mathematics.

Computer learns to write captions with feeling

1 min read ·
Mohamed Elhoseiny and his PhD student Youssef Mohamed are teaching AI to capture the full spectrum of human emotions when annotating artwork in order to reduce emotional bias in computer-generated captions.

Yuchen Li

Ph.D. Student, Computer Science
Yuchen Li is a PhD Student under the supervision of Professor Mohamed Elhoseiny in Vision-CAIR Group at Visual Computing Center at King Abdullah University of Science and Technology (KAUST). Education and Early Career Before joining KAUST, Yuchen was a research intern at iFLYTEK, an intelligent speech and artificial intelligence company in Hefei, China, for three months. He was a Rocket MQ open source contributor and Alibaba summer of code student developer with rich research and engineering experience. He visited NUS, UBC, THU, and other prestigious universities for academic exchanges with a

Markus Hadwiger

Professor, Computer Science
Markus Hadwiger is a Professor of Computer Science at the King Abdullah University of Science and Technology (KAUST). A founding member of the University, he conducts fundamental and applied research in scientific visualization and visual computing.

Ivan Viola

Professor, Computer Science
Professor Viola’s expertise lies in scientific visualization and visual computing. His research focuses on multiscale visualization and modeling of complex biological systems, ranging from cellular organisms to atomistic-level details.

Hisham Alhussain

Hisham Alhussain is a Computer Science Master Student under the supervision of Professor Markus Hadwiger at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Hisham received his bachelor's degree in Computer Science from the University of Massachusetts at Amherst in 2018. He is also a Software Developer at IBM. Research Interest He's currently focusing on Scientific Visualization and Visual Computing. He is proficient in Python, Java, and has professional experience in React/React-Native and mobile and web development

Dominik L. Michels

Associate Professor, Computer Science
Professor Michels is a leading computer scientist who proactively designs strategies to face upcoming digitization challenges. His outstanding research has been recognized by the scientific community within and beyond KAUST.

Cristhopper Armenta

Originally from Mexico, Cristhopper is a computing professional who has a keen interest in mobile platforms like iOS, Android and Windows Phone. His specialties and interests range from visual computing (visualization), computer graphics, mobile technology development and technology business development. After some contractor work with two companies in Mexico and the US, he now works for IBM in the US. ''I worked as a subcontractor for IBM and got to meet some of the key people for the project who really liked my work and ended up offering me a full-time position,'' he told us. How was life at
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Big Data Analyses in Evolutionary Biology

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B9 H2

This event is organized by CBRC with financial support from the KAUST Office of Sponsored Research

Exploring FPGAs for Virtualized In-Network Acceleration

Dr. Suhaib Fahmy, Associate Professor, Computer Engineering, University of Warwick, UK

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B2 B3 A0215

Abstract With increasing connectivity and reliance on machine intelligence to process ever-growing amounts of data, the question of how to arrange the required connectivity and computation arises. Traditional cloud computing approaches that centralize compute capability in a data center do not scale well to large scale distributed data sources that must then transmit data over constrained networks. Similarly, computing at the very edge of the network is often constrained by limited computational capacity and a lack of access to shared data. In-network computing has been proposed as a way of

EF-BV: distributed optimization with compressed communication

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B9 L2 R2322 H1

In the big data era, it is necessary to rely on distributed computing. For distributed optimization and learning tasks, in particular in the modern paradigm of federated learning, specific challenges arise, such as decentralized data storage. Communication between the parallel machines and the orchestrating distant server is necessary but slow. To address this main bottleneck, a natural strategy is to compress the communicated vectors. I will present EF-BV, a new algorithm which converges linearly to an exact solution, with a large class of deterministic or random, biased or unbiased compressors.

CEMSE Big Data Open Day shows off fascinating discoveries

1 min read ·
The fifth-anniversary event marked CEMSE's biggest successes and featured 20 KAUST discoveries, with more than 1,000 visitors from the community attending the event."Big data has many applications: to understand medicine better; to manage food supplies, and to connect objects. Data is at the center of everything," said Dean Mootaz Elnozahy of the University's Computer, Electrical, Mathematical Science and Engineering (CEMSE) Division at the CEMSE Big Data Open Day held on December 4, 2016.

Professors discuss big data and human health in latest Sci-Café

1 min read ·
​Professors Takashi Gojobori, Xin Gao and Robert Hoehndorf spoke about big data and human health at the Sci-Café event held on February 27. Their talk, which was moderated by James Calvin, Vice President for Academic Affairs, aimed to answer the question, "Can Big Data Solve My Health Problems?" This event was open to all members of the KAUST community.

The awesome scope of big data

1 min read ·
Sifting through huge amounts of data may bring a better understanding of whale shark social structures, protein targets for drug therapies and disease-causing genes.

Facebook AI new language explained by KAUST Professor Xiangliang Zhang

1 min read ·
That is not a Messenger troll, but the actual conversation between two chatbots by the Facebook AI Research (FAIR). The two robots, Bob and Alice, were thought the art of negotiation apples and books. They had been instructed to work out how to negotiate between themselves and improve their bartering as they went along. But, after leaving the pair alone, they start talking in this uncompressible but yet effective vocabulary.

Exploring science's fourth paradigm

1 min read ·
The recent KAUST Research Conference: Computational and Statistical Interface to Big Data brought together leading computer science researchers and statistical experts to discuss the current state and future of data science. Held on campus from March 19 to 21, the conference covered such data science topics as succinct data representation and storage; big data visualization; parallel and distributed algorithms for inference and optimization; and analysis of large graphs and networks.

Alumni Focus: Ahmed Abdulmajeed Alabdulkarim, M.S. '11

1 min read ·
Ahmed Abdulmajeed Alabdulkarim earned a master's degree from KAUST in 2011. His research interests are in the field of big data, artificial intelligence and machine learning, and he is now pursuing his Ph.D. at the Massachusetts Institute of Technology (MIT) in the U.S. He received his bachelor's degree with honors in computer science from King Saud University, where he graduated at the top of his class. From a young age, Alabdulkarim was passionate about computers. Studying computer science was his drive and motivation to continue his university studies. He explained that he chose KAUST for

Querying big data just got universal

1 min read ·
A universal query engine for big data that works across computing platforms could accelerate analytics research.

Accelerating the grapevine effect

1 min read ·
By looking at classical gossip algorithms from a novel perspective, KAUST Professor Peter Richtarik has found a way to significantly speed up gossip-based information sharing, and in the process, he discovered new applications for this efficient mathematical approach. Gossip involves the sharing of information between individuals in a network and can be applied mathematically in both human social networks and data networks, such as distributed sensors. “A network is a collection of nodes, each connected to other nodes via links,” explains Richtarik. “In social networks, for instance

AI learns complex gene-disease patterns

1 min read ·
A deep learning model improves the ability to identify genes potentially involved in disease.

KAUST Professor Peter Richtárik wins Distinguished Speaker Award

1 min read ·
Peter Richtárik, KAUST professor of computer science, recently received a Distinguished Speaker Award at the Sixth International Conference on Continuous Optimization (ICCOPT 2019) held in Berlin from August 3 to 8. ICCOPT 2019 was organized by the Mathematical Optimization Society and was hosted this year by the Weierstrass Institute for Applied Analysis and Stochastics.

CEMSE Division startups in focus: Peregrine Genomics

1 min read ·
The future has already arrived when it comes to the most exciting and promising field of modern medicine—precision medicine.

Training agents to walk with purpose

1 min read ·
By training a search agent to make smarter exploratory decisions, relational data can be classified more accurately and efficiently.

Keep the data coming

1 min read ·
A continuous data supply ensures data-intensive simulations can run at maximum speed.

Professor Ghanem wins 2020 Abdul Hameed Shoman Award for Arab Researchers

1 min read ·
Bernard Ghanem, KAUST associate professor of electrical and computer engineering and computer science, recently received the 2020 Abdul Hameed Shoman Award for Arab Researchers for ''Machine Learning and Big Data.''

Cloud simulations get a dose of realism

1 min read ·
A focus on the fundamental physics of cloud formation leads to highly realistic simulations of different types of clouds.

Machine learning at speed

1 min read ·
Optimizing network communication accelerates training in large-scale machine-learning models.

The IEEE CS Global Student Challenge seeks top KAUST CS talent

1 min read ·
The annual IEEE Computer Society Global Student Challenge provides an avenue for students worldwide to create innovative solutions to big data problems. This year’s competition, IEEE CS GSC 2021, encourages each individual or team (max three students) to present solutions to pre-determined problem/issue statement of relevance in CS today.

Drop the zeros for a performance boost

1 min read ·
An eight-fold speed up of deep machine learning can be achieved by skipping the transmission of zero values.

Code of collaboration

1 min read ·
David Keyes's and Matteo Ravasi's cross-disciplinary project results in improved efficiency for seismic processing, with promising applications for the energy industry.

Zuhair Yarub Khayyat

Zuhair Yarub Khayyat obtained his Ph.D. degree in Computer Science under the supervision of Professor Panagiotis Kalnis at InfoCloud Research Group (CLOUD) in King Abdullah University of Science and Technology (KAUST) in 2016. Research Interest Zuhair's research interests included Big Data Management, Parallel and distributed data processing, Cloud Computing and Graph Analytics. His main experience was focused on problem-solving and algorithm development for processing large data in distributed environments. He is a reliable team player with a methodological mindset with the ability to utilize

Xin Gao

Program Chair, Computer Science
Professor Gao is a world-leading expert in developing novel AI solutions for challenges in biology, biomedicine, and health and wellness, with a focus on AI-based drug development, large language models in biomedicine, biomedical imaging analysis, and omics-based disease detection and diagnostics.

Ruogu Ding

Ruogu Ding obtained his Master's Degree in Computer Science under the supervision of Professor Panagiotis Kalnis at InfoCloud Research Group (CLOUD) in King Abdullah University of Science and Technology (KAUST). Prior to that, Ruogu received his Bachelor's degree in Computer Science from Peking University in July 2009. He worked as a research assistant on large-scale sequence assembly algorithms 2009-2010 at Peking University. Ruogu is currently serving as a Technology Partner at MiningLamp, a fast-growing startup aiming to provide enterprises with big data solutions. He worked on Algorithm

Peter Richtarik

Professor, Computer Science
Professor Richtárik specializes in big data optimization and machine learning. He is known for his work on randomized coordinate descent algorithms, stochastic gradient descent and federated learning.

Ahmed Abdulmajeed Alabdulkarim

Ahmed Abdulmajeed Alabdulkarim earned a master's degree from KAUST in 2011. His research interests are in the field of big data, artificial intelligence and machine learning, and he is now pursuing his Ph.D. at the Massachusetts Institute of Technology (MIT) in the U.S. He received his bachelor's degree with honors in computer science from King Saud University, where he graduated at the top of his class. From a young age, Alabdulkarim was passionate about computers. Studying computer science was his drive and motivation to continue his university studies. He explained that he chose KAUST for
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Novel Data Mining Methods for Virtual Screening of Biological Active Chemical Compounds by Othman Soufan

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H2 B9

Abstract Drug discovery is a process that takes many years and hundreds of millions of dollars to reveal a con dent conclusion about a specific treatment. Part of this sophisticated process is based on preliminary investigations to suggest a set of chemical compounds as candidate drugs for the treatment. Computational resources have been playing a significant role in this part through a step known as virtual screening. From a data mining perspective, the availability of rich data resources is key in training prediction models. Yet, the difficulties imposed by big expansion in data and its

Interactive High-Quality Visualization of Large-Scale Particle Data

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B2 L5 R5209

Large-scale particle data sets, such as those computed in molecular dynamics (MD) simulations, are crucial to investigating important processes in physics and thermodynamics. The simulated atoms are usually visualized as hard spheres with Phong shading, where individual particles and their local density can be perceived well in close-up views. However, for large-scale simulations with 10 million particles or more, the visualization of large fields-of-view usually suffers from strong aliasing artifacts, because the mismatch between data size and output resolution leads to severe under-sampling of the geometry.

Point cloud and volume data reconstruction and visualization

Dr. Ciril Bohak, Postdoctoral Researcher, Faculty of Computer and Information Science, University of Ljubljana, Slovenia

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B1 L2

Abstract Our ongoing research on the reconstruction of aerial point cloud data includes reconstructing the parts of the data that are not captured by the airplane mounted LiDAR system due to the shadowing effect and lack of reflections. We are developing methods for reconstructing (1) building walls, (2) reconstruction of bridges and underlying terrain; (3) reconstruction of water surfaces of lakes and rivers, and (4) reconstruction of terrain overhangs. In all the cases we are merging the point cloud data with cadaster data or digital terrain model data for filling the gaps in the point cloud

KAUST Cell Visualization Summit 2019

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B9 H2 R2325

You are invited to attend the KAUST Cell Visualization Summit 2019 organized by the Nanovisualization group from the Visual Computing Center (VCC). The summit will focus on the technological advancement that can lead to an in-silico virtual and visual representation by integrating current knowledge into a unified structural and dynamic model of a living cell. Each keynote speaker will relate to one of the technological aspects (modeling, visualization, and interaction). Keynote speakers Arthur Olson Graham Johnson Anders Ynnerman For more information about the summit, please click here. If you

Optimal Reference Frames, Scaling and Features in Visualization

Prof. Holger Theisel, Visual Computing, Magdeburg University

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B9 L2 H1 R2322

In Visualization, the success or failure of an analysis often depends on the choice of some subtle parameters or design choices. While simple heuristics are often sufficient, in some cases they make the analysis miserably fail. We present three approaches in visualization where a careful choice of optimal parameters results in completely new algorithms: 1) the choice of a reference frame for finding objective vortices in flow visualization, 2) the choice of a scaling of high-dimensional data sets for finding linear projections to 2D in information visualization, and 3) the choice of a feature definition along with numerical extraction methods for visualizing recirculation phenomena in flows.

Visualization Methods for Graphs&Networks

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B9 L2 H2 H2

Inevitably, the projection of most graph structures on two-dimensional screens will create errors and therefore visually wrong impressions. In the past, two types of methods have been developed to minimize projection errors and distribute them in a visually pleasing way. The first group of methods, force-directed layouts, interpret the links of a graph as physical springs, while stress-based methods minimize an energy function, which aims to map graph distances faithfully.

A breeding ground for successful start-ups

1 min read ·
State-of-the-art facilities and world-class research projects give rise to thriving start-up companies based at KAUST.

Nanovisualization Research Group is hiring

1 min read ·
Nanovisualization Research Group is hiring scientific staff on the levels of Research Scientist, Postdoctoral Fellow, and Graduate Student. Follow the links to apply for the respective position: Research Scientist position Postdoctoral Fellow Graduate Student

Scanning in the Fourth Dimension

1 min read ·
Three-dimensional (3D) computed tomography is a widely used technology that visualizes an object's external and internal structure by assembling a series of two-dimensional images taken sequentially across or around it. However, as anyone who has had a medical magnetic resonance imaging scan will recall, this type of 3D reconstruction requires the subject to be motionless throughout the capture process, which can take minutes. Capturing a 3D structure that changes or deforms over time is much more difficult, and existing approaches often yield reconstructions marred by image artifacts and

Professor Ivan Viola Joins the Visual Computing Center

1 min read ·
We are proudly announcing that Professor Ivan Viola has joined VCC as an Associate Professor of Computer Science in November, 2018. He is establishing the Nano-visualization Research Group as a part of the Visual Computing Center. Prof. Viola has a master and Ph.D. from TU Wien in Austria. He has received a series of honors and recognition for his contribution to computing visualization such as the Austrian Computer Graphics Award 2016 for the Best Technical Solution, and the 1st Place Eurographics Dirk Bartz Prize for Visual Computing in Medicine 2013. Prof. Viola was attracted to KAUST for

The NANOVIS team wins the 1st Place in Graph Drawing Contest 2019

1 min read ·
The NANOVIS team has won the 1st Place in Graph Drawing Contest 2019 under the category of "Creative Topic #2 Meal Ingredients" on their graph "Worldmap of Food". The contest took place in the 27th International Symposium on Graph Drawing and Network Visualization (GD'19) that was held from September 17 to 20, 2019 in Průhonice/Prague, Czechia. Check the full poster:

Three papers with contributions from the NANOVIS team will be presented at IEEE VIS

1 min read ·
Three papers with contributions from the NANOVIS team will be presented at IEEE VIS 2019 that will be held from 20 to 25 October 2019 in Vancouver, Canada. Multi-Scale Procedural Animations of Microtubule Dynamics Based on Measured Data ScaleTrotter: Illustrative Visual Travels across Negative Scales Data-aware Continuous Colormap Refinement for 2D Scalar Field Visualization IEEE VIS 2019 is the premier forum for advances in visualization for academia, government, and industry. This week-long event brings together researchers and practitioners with a shared interest in visualization.

KAUST Ph.D. student Jinhui Xiong wins best paper award

1 min read ·
KAUST computer science Ph.D. student Jinhui Xiong recently won the best paper award at the 24th International Symposium on Vision, Modeling, and Visualization. The symposium took place from September 30 to October 2, 2019, at the University of Rostock, Germany, and provided the opportunity for researchers to discuss a wide range of topics in computer science, including computer graphics, vision, visualization and visual analytics.

Thomas Hollt

Postdoctoral Research Fellow, High Performance Visualization Group
Thomas Hollt was a Postdoctoral Research Fellow at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Currently, he’s an Assistant Professor at Computational Biology Center in Leiden University Medical Center, The Netherlands. Education and Early Career Thomas joined KAUST as a Ph.D. student in Computer Science under the supervision of Professor Markus Hadwiger. After he graduated in 2013, he worked as a Postdoctoral Research Fellow in Visual Computing Center. Later on, he joined the Earth Fluid Modeling and Prediction Group at KAUST as a Research

Ronell Sicat

Research Scientist, Computer Science
Ronell Sicat is a Research Scientist in the High Performance Visualization Group working with Professor Markus Hadwiger at King Abdullah University of Science and Technology (KAUST). His research interests are in large-scale data visualization and analysis, immersive analytics, and computer graphics.

Mohamed Ibrahim

Postdoctoral Research Fellow, Computer Science
Mohamed Ibrahim is a Postdoctoral Research Fellow in High-Performance Visualization Group (VCCVIS) working with Prof. Markus Hadwiger at King Abdullah University of Science and Technology (KAUST). Education and Early Career He earned his Bachelor’s degree in Computer Engineering from the American University in Cairo (AUC) in 2010. Later on, he obtained a Master’s degree in Computer Science from KAUST in 2012. He graduated from KAUST with a Ph.D. in Computer Science in 2019. Research Interest Mohamed Ibrahim's research is concerned with Large-Scale data visualization. He's especially interested

Michael Birsak

Research Scientist, Computer Science
Michael Birsak is a Research Scientist working with Professor Peter Wonka at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST).

Matej Mlejnek

Research Scientist, Computer Science
Matej Mlejnek is a Research Scientist in the High Performance Visualization Group (HPV) working with Professor Markus Hadwiger at King Abdullah University of Science and Technology (KAUST). Education and Early Career He obtained his bachelor degree in Automation and Control from Slovak University of Technology in Slovakia in1999. After that, he joined Vienna University of Technology in Austria and received both his master (2003) and doctoral (2006) degrees in Computer Science. Research Interest Matej is interested in scientific visualization. Selected Publications Matej Mlejnek has many

Markus Hadwiger

Professor, Computer Science
Markus Hadwiger is a Professor of Computer Science at the King Abdullah University of Science and Technology (KAUST). A founding member of the University, he conducts fundamental and applied research in scientific visualization and visual computing.

Jens Schneider

Research Scientist, Computer Science
Jens Schneider was a Staff Scientist at the Visual Computing Center in King Abdullah University of Science and Technology (KAUST). Currently, he’s an Assistant Professor in College of Science and Engineering, Hamad Bin Khalifa University (HBKU), Qatar. Education and Early Career Jens Schneider received a diploma equivalent to Master degree in Computer Science from RWTH Aachen in Germany, 2004. He earned his Ph.D. in Computer Science from Technical University Munich, Germany, in 2009 Dr. Schneider joined KAUST in 2009 as a Postdoctoral Research Fellow. Later on, he was promoted to Research

Donggang Jia

Ph.D. Student, Computer Science
Donggang Jia is a Ph.D. student in the Computer Science Program under the supervision of Professor Ivan Viola in the Nanovisualization Research Group at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Donggang has a Master's Degree in Artificial Intelligence from the University of Southampton, United Kingdom. Before that, he received his Bachelor's degree in Computer Science at the Beijing University of Posts and Telecommunications, China. Research Interest Donggang is focusing on computer graphics and visualization

Dawar Khan

Postdoctoral Research Fellow, Computer Science
Dawar Khan has been working as a Postdoctoral researcher with Professor Ivan Viola in the Nanovisualization Research Group, at Visual Computing Center, KAUST - King Abdullah University of Science and Technology, Saudi Arabia, since April 2022. Previously he worked as assistant professor at Interactive Media Design Lab, Nara Institute of Science and Technology (NAIST), Japan and at the University of Haripur, Pakistan. Education and Early Career He received the BS and MS degrees in computer science from the University of Malakand, Pakistan, in 2011 and 2014, respectively, and the PhD degree in

Cristhopper Armenta

Originally from Mexico, Cristhopper is a computing professional who has a keen interest in mobile platforms like iOS, Android and Windows Phone. His specialties and interests range from visual computing (visualization), computer graphics, mobile technology development and technology business development. After some contractor work with two companies in Mexico and the US, he now works for IBM in the US. ''I worked as a subcontractor for IBM and got to meet some of the key people for the project who really liked my work and ended up offering me a full-time position,'' he told us. How was life at

Anna Fruehstueck

Anna Fruehstueck was a Ph. D. candidate under the supervision of Prof. Peter Wonka at King Abdullah University of Science and Technology (KAUST) and successfully defended her PhD in Computer Science on April 17, 2023. Education and Early Career Anna Fruehstueck obtained a Bachelor’s Degree in Media Informatics and Visual Computing from Vienna University of Technology in Austria in 2011. Later on in 2015, she received her M.Sc. degree in Visual Computing from Vienna University of Technology in Austria. During her studies, Anna worked as software developer at SimVis in Vienna, Austria. And in

Amani Ageeli

Ph.D. Student, Computer Science
Amani Ageeli is a Ph.D. student in the Computer Science program under the supervision of Professor Markus Hadwiger at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Amani received her bachelor's degree in Computer Science from Umm Al-Qura University in Makkah, Saudi Arabia in 2016. She joined KAUST to continue her studies and earned a master's degree in Computer Science in 2019. Currently, she's working on directed research with Prof. Markus Hadwiger in the High-Performance Visualization Group. Research Interest Amani

Amal Aboulhassan

Amal Aboulhassan was a Ph.D. student in Computer Science under the supervision of Professor Markus Hadwiger at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). She is the founder of MaterialSolved startup located in Alexandria, Egypt. Education and Career Amad received her Master degree in Computer Graphics from Alexandria University, Egypt in 2005. She joined KAUST and earned her Ph.D. in Computer Science in 2017. She was a part of High Performance Visualization Research Group under the supervision of Professor Markus Hadwiger. Before joining

Alexandre A. Kouyoumdjian

Research Scientist, Computer Science
Dr. Alexandre A. Kouyoumdjian has joined the Nanovisualization Research Group as a Research Scientist working with Professor Ivan Viola. His main research interests are Visualization & Computer Graphics, Human-Computer Interaction & Virtual Reality, and Microarchitecture. Dr. Alexandre A. Kouyoumdjian has also worked in the industry as an R&D Consultant for a start-up that develops VR applications meant to ease chronic pain and a graphics programmer for a game studio. Education Profile Ph.D., Computer Science, Université Paris-Saclay,FRANCE, 2018 M.S., Computer Science (Bioinformatics and
<> Computational biology (23)

Novel Data Mining Methods for Virtual Screening of Biological Active Chemical Compounds by Othman Soufan

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H2 B9

Abstract Drug discovery is a process that takes many years and hundreds of millions of dollars to reveal a con dent conclusion about a specific treatment. Part of this sophisticated process is based on preliminary investigations to suggest a set of chemical compounds as candidate drugs for the treatment. Computational resources have been playing a significant role in this part through a step known as virtual screening. From a data mining perspective, the availability of rich data resources is key in training prediction models. Yet, the difficulties imposed by big expansion in data and its

Mining Genome-Scale Growth Phenotype Data through Constant-Column Biclustering

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B3 L5 R5209

Growth phenotype profiling of genome-wide gene-deletion strains overstresses conditions can offer a clear picture that the essentiality of genes depends on environmental conditions. In this dissertation, we first demonstrate that detecting such "co-fit" gene groups can be cast as a less well-studied problem in biclustering, i.e., constant-column biclustering. Despite significant advances in biclustering techniques, very few were designed for mining in growth phenotype data.

Omics-based Metastasis Prediction using Machine Learning and Deep Learning

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KAUST

Knowing metastasis is the primary cause of cancer-related deaths incentivized research to unravel the complex cellular processes that drive the metastasis. Advancement in technology and specifically the advent of high-throughput sequencing provides knowledge of such processes. This knowledge led to the development of therapeutic and clinical applications. In this regard, predicting metastasis onset has also been explored using artificial intelligence (AI) approaches that are machine learning (ML), and more recently, deep learning (DL).

Drug Repositioning through the Development of Diverse Computational Methods using Machine Learning, Deep Learning, and Graph Mining

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KAUST

In this dissertation, we combined artificial intelligence and machine/deep learning with chemical and biological properties to develop several computational methods to solve biomedical domain problems, specifically drug repositioning, and demonstrated their efficiencies and capabilities. We developed three network-based DTI prediction methods using machine learning, graph embedding, and graph mining. These methods significantly improved prediction performance, and the best-performing method even reduces the error rate by more than 33% across all datasets compared to the best state-of-the-art method. As it is more insightful to predict continuous values that indicate how tightly the drug binds to a specific target, we conducted a comparison study of current regression-based methods that predict drug-target binding affinities (DTBA). Our methods demonstrated their efficiency and capability by achieving high prediction performance and identifying therapeutic targets for several cancer types. We further conducted a lung cancer case study of findings that support the novel predicted targets.

A personal path from mathematics to biomedical research

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B2 L5 R5209

A personal presentation describing the role of computational biology and bioinformatics in addressing state-of-the-art biomedical challenges. First, I will provide an overview of the transition between a Ph.D. in Mathematics to a postdoc in Computational Biology: How did it happen? What were the challenges? Secondly, I will briefly present several current case studies where computational biology (in several flavors) is core to understanding novel biological data related to multi-omic data analysis, spatial profiling, gene therapy, and more.

Amin Allam (CEMSE PhD) wins the second prize in the DREAM Challenge 1B

1 min read ·
Amin Allam (CEMSE PhD student in the InfoCloud group) supervised by Prof Panos Kalnis has won the second prize in the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge 1B. Amin Allam utilized Bayesian inference to achieve the 2nd place.

Alumni Feature: Arturo Magana Mora

1 min read ·
CBRC Ph.D. graduate Arturo Magana Mora is currently a postdoctoral researcher at the prestigious National Institute of Advanced Industrial Science and Technology (AIST) in Tokyo, Japan.

Siqing Hou defends his Master’s thesis

1 min read ·
Siqing Hou, a Master's student under the supervision of Associate Professor Xin Gao, successfully defended his thesis.

CBRC goes to ECCB 2018

1 min read ·
The European Conference on Computational Biology (ECCB) is one of the most important conferences in the field of bioinformatics and computational biology. Held in a different European city annually, this year's conference will take place in the historic city of Athens, Greece on September 8-12, 2018.

Meet KAUST prospective student: Xiaopeng Xu

1 min read ·
Xiaopeng Xu is a computer science graduate who will join KAUST in the fall of 2020. Xu obtained a master’s degree in computer science from KAUST, and a bachelor’s degree in bioinformatics from Huazhong University of Science and Technology, respectively. He will join KAUST in the fall of 2020 as a Ph.D. candidate and a member of the KAUST Structural and Functional Bioinformatics group under the supervision of Professor Xin Gao.
KAUST-CEMSE-CS-SFB-Zhongxiao-Li

Zhongxiao Li

Ph.D. Student, Computer Science
Zhongxiao Li is a Ph.D. student under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST).​ Zhongxiao obtained his Bachelor of Science degree from Southern University of Science and Technology, China in 2018. Research Interests Zhongxiao's research interests include Bioinformatics and Computational Biology.

Yongkang Long

Ph.D. Student, Computer Science
Yongkang Long is a Ph.D. student under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Research Interests Yongkang's research interests include Bioinformatics and Computational Biology.

Xin Gao

Program Chair, Computer Science
Professor Gao is a world-leading expert in developing novel AI solutions for challenges in biology, biomedicine, and health and wellness, with a focus on AI-based drug development, large language models in biomedicine, biomedical imaging analysis, and omics-based disease detection and diagnostics.

Xiaopeng Xu

Ph.D. Student, Computer Science
Xiaopeng Xu is a Ph.D. candidate in the KAUST Structural and Functional Bioinformatics group under the supervision of Professor Xin Gao. Before joining Ph.D. program, Xiaopeng obtained a master’s degree in computer science from King Abdullah University of Science and Technology (KAUST), and a bachelor’s degree in bioinformatics from Huazhong University of Science and Technology, respectively. Research Interests Xiaopeng's research interests included Bioinformatics, artificial intelligence and computational biology.

Wenkai Han

Ph.D. Student, Computer Science
Wenkai Han is a Ph.D. student under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Wenkai obtained his Bachelor of Science degree from the University of Science and Technology of China in 2018. Research Interests Wenkai's research interests include Bioinformatics and Computational Biology.

Vasiliki Kordopati

Vasiliki Kordopati obtained Master degree in Computer Science under the co-supervision of Professor Boon Ooi at Photonics Laboratory and Prof. Xin Gao at Structural and Functional Bioinformatics Group (SFB) from King Abdullah University of Science and Technology (KAUST). Vasiliki is a co-founder of Oæsis, a King Abdullah University of Science and Technology (KAUST) startup spinout, where she is responsible for product development. A computer engineer by training, she received a bachelor's degree in Computer Engineering from the University of Patras, Greece. Research Interest Vasiliki's

Somayah Albaradei

Ph.D. Student, Computer Science
Somayah Albaradei is a Ph.D. student at Structural and Functional Bioinformatics Group (SFB) under the supervision of Professor Xin Gao at King Abdullah University of Science and Technology (KAUST). ​Somayah obtained her master's degree in computer science from the University of Manitoba, Canada in 2014. Professional Profile 2014: Lecturer at College of Computing and Information Technology-King Abdulaziz University - Jeddah - Saudi Arabia 2010- 2011: Teacher Assistant at College of Computing and Information Technology-King Abdulaziz University - Jeddah - Saudi Arabia 2009-2010: Collaborator

Siqing Hou

Siqing Hou obtained his Master's degree in Computer Science under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Master Thesis, 2018: Neural Inductive Matrix Completion for Predicting Disease-Gene Associations Research Interests Siqing Hou was working in the fields of Bioinformatics, Computational Biology and Machine Learning. Professional Profile Research Student, University of Birmingham, Jul 2015 - Aug 2018 Software Effort Estimation, Machine Learning Education Profile M.Sc. Computer

Sheng Wang

Sheng Wang worked as a Research Scientist at Prof. Xin Gao's Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Sheng got his Ph.D. in Bioinformatics from the Institute of Theoretical Physics at the Chinese Academy of Sciences in 2010. He was then a postdoc fellow at Toyota Technological Institute in Chicago during 2010 and 2014. During 2014 and 2017, he was a research professional at the Department of Human Genetics at the University of Chicago. Since March 2017, he joined the SFB group as a research scientist. Currently, Sheng is a

Mario Messih

Mario Messih is a Former Visiting student from the Sapienza University of Rome, Italy at Professor Xin Gao's Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Mario Finished his Ph.D. study with Prof. Anna Tramontano at the Sapienza University of Rome, Italy, and currently, Mario is a postdoc fellow at Yale University. Research Interests Mario's research interests included Bioinformatics and computational biology. His main interests lie at the intersection of machine learning, statistics and their applications on analyzing large

Heba Abu Samra

Heba Abu Samra obtained her Master's degree in Computer Science under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology (KAUST). Master Thesis (2013): A Comparative Study of Feature Selection and Classification Methods for Gene Expression Data Research Interests Heba's research interests included Bioinformatics and Computational Biology.

Haoyang Li

Ph.D. Student, Computer Science
Haoyang Li is a Ph.D. student in the KAUST Structural and Functional Bioinformatics research group under the supervision of Professor Xin Gao. Before joining KAUST, Haoyang majored in Bioinformatics and graduated from Jilin University (JLU), located in Changchun, Jilin, China. Research Interests Haoyang focuses on computational biology and bioinformatics. As a visiting student before, Haoyang did some research in the field of medical image analysis and metabolic reprogramming. Recently, Haoyang focuses on spatial transcriptomics techniques and has an interest in developing related methods.

Amin Allam

Amin Allam obtained his Ph.D. in Computer Science at the King Abdullah University of Science and Technology (KAUST) under the supervision of Prof. Panagiotis Kalnis in 2017. Amin is a lecturer in the Computer Science department, Faculty of Computers and Information, Cairo University, Egypt. He got his bachelor's degree in Computers and Information in 2003 from Cairo University. Then, he got a Master's degree titled "An Arabic-Disambiguating Engine Based on Statistical Approaches" in Computer Science from the same university in 2008. Research Interest Amin's research interests include
<> genomics (18)

Contributions to In Silico Genome Annotation

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B3 L5 R5209

Abstract Genome annotation is an important topic since it provides information for the foundation of downstream genomic and biological research. It is considered as a way of summarizing part of existing knowledge about the genomic characteristics of an organism. Annotating different regions of a genome sequence is known as structural annotation while identifying functions of these regions are considered as a functional annotation. In silico approaches can facilitate both tasks that otherwise would be difficult and time-consuming. This study contributes to genome annotation by introducing

Novel Computational Methods that Facilitate Development of Cyanofactories for Free Fatty Acid Production by Olaa Motwalli

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B3 L5 R5209

Abstract Finding a source from which high-energy-density biofuels can be derived at an industrial scale has become an urgent challenge for renewable energy production. Some microorganisms can produce free fatty acids (FFA) as precursors towards such high-energy-density biofuels. In particular, photosynthetic cyanobacteria are capable of directly converting carbon dioxide into FFA. However, current engineered strains need several rounds of engineering to reach the level of FFA production for it to be commercially viable. Thus, new chassis strains that require less engineering are needed

VSIM: Visualization and Simulation of Genomes for Premarital Testing

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B3 L5 R5209

Abstract Interpretation and simulation of the large-scale genomics data are very challenging, and currently, many web tools have been developed to analyze genomic variation which supports automated visualization of a variety of high throughput genomics data. We have developed VSIM an automated and easy to use web application for interpretation and visualization of a variety of genomics data, it identifies the candidate disease variants by referencing to four databases Clinvar, GWAS, DIDA, and PharmGKB, and predicted the pathogenic variants. Moreover, it investigates the attitude towards

AI4GH Seminar Series - VSIM: Visualization and Simulation of Genomes for Premarital Testing

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B2 R5220

Interpretation and simulation of the large-scale genomics data are very challenging, and currently, many web tools have been developed to analyze genomic variation which supports automated visualization of a variety of high throughput genomics data.

Large-scale Text and Data Integration for Cancer Genomics Research

Prof. D, Rebholz-Schuhmann (University Cologne, Germany​)​

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B19 L3 H2

Abstract This talk will give an overview of data and text mining in the life science domain, but will also point out the use of semantic technologies for other domains. Such solutions cover the integration of heterogeneous data sources based on Semantic Web standards (e.g., TCGA, Cosmic, others), the development of smart technologies (e.g., stream processing), graph and literature analytics, and open data initiatives. Application domains are biomedical solutions in cancer genomics and targeted projects together with industrial partners exploiting Semantic Web technologies in general. The

Digital Health: promises and pitfalls

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B9 L3 R3223

This talk will try to clarify some misconceptions about what digital health (DH) is, and what it should not be.

Making the silent genes speak

1 min read ·
Regulating an overlooked set of genes may help biologists manipulate cells to find new therapy options.

Honey bee behavior is in the genes

1 min read ·
An integrated set of genetic mechanisms controls whether bees behave as foragers or nurses.

Exploring protein form to find function

1 min read ·
Existing systems for predicting protein structure are outperformed by a newly developed method.

Enhancing understanding of genetic regulation

1 min read ·
Researchers round up clues to track down enhancers.

Student life: studying and working at the Computational Bioscience Research Center (KAUST)

1 min read ·
For Ph.D. students Arturo Magana Mora, Haitham Ashoor and Olaa Motwalli, April 2017 was a particularly nerve-wracking month. After years of hard work and sleepless nights, they finally defended their thesis, bringing them one step closer to graduation.

Mining Red Sea bacteria for industrial potential

1 min read ·
Genome sequencing of two Red Sea bacteria highlights their potential as industrial workhorses.

Azza Althagafi defends her Master's thesis

1 min read ·
Azza Althagafi, a student under the supervision of Assistant Professor Robert Hoehndorf, defended her Master's thesis on November 1, 2018.

Studying COVID-19’s envelope protein

1 min read ·
A likeness between genes of the SARS and COVID-19 viruses could inform research into potential treatments.

Simulating evolution to understand a hidden switch

1 min read ·
Some organisms evolve an internal switch that can remain hidden for generations until stress flicks it on.

Sarah Alghamdi

Ph.D. Student, Computer Science
Sarah Alghamdi is a Ph.D. candidate in the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Robert Hoehndorf. Research Interests Sarah's research interests include applications of artificial intelligence and using statistical methods to genomics and healthcare. Professional Profile Program Committee for the International Conference of Biomedical Ontology (ICBO 2022) Program Committee for the International Society of Molecular Biology (ISMB 2022) - Bio-Ontology track 2018-2021: Teaching Assistant positions: CS

Olaa A. Motwalli

Research Interests Olaa Motwalli is a Ph.D. student in Computational Science and a member of the Computational Bioscience Research Center (CBRC) at KAUST. She obtained her Bachelor's degree in Computer Science at King Abdul Aziz University in Jeddah and obtained her Master's degree in Computer and Software Engineering at Widener University in the USA. Her research interests are mainly the development and computational support for the design of sustainable biofuel production via microbial cell factories through machine learning, graph mining, and algorithm development, genome-scale metabolic

Jesper Tegner

Professor, Bioscience
Jasper Tegner is an M.D./Ph.D., innovator and professor with over 350 publications, dedicated to exploring the interconnected mechanisms underlying life, intelligence and the mind. His research aims to advance fundamental progress in artificial intelligence by moving beyond engineering to understand the intrinsic modes of operation within cells, between cells and within the brain.
<> Scientific Visualization (16)

Thoughts on how to Effectively Explore Visual Representations in Mixed Reality

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B9 L2 R2322 H1

In this talk I will report on various research projects that I carried out with my students to better understand the interaction landscape and will report on lessons we learned. I will focus mostly on AR-based setups with application examples from physical flow visualization, molecular visualization, visualization of particle collisions, biomolecular dynamics in cells, and oceanography. I will show interaction techniques that rely on purely gestural interaction, phones or tablets as input and control devices, and hybrid setups that combine traditional workstations with AR views. I will discuss navigation, data selection, and visualization system control as different interaction tasks. With this overview I aim to provide an understanding of typical challenges in immersive visualization environments and how to address some of these challenges.
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Xingdi Zhang

Ph.D. Student, Computer Science
Visualization: Riemannian Geometry based observer relative vortex extraction; Computer vision: depth estimation, point cloud understanding. Computer graphics: discrete geometry, graphics engine, etc.

Sara Shaheen

Sara Shaheen was a Ph.D. student in Computer Science under the supervision of Professor Bernard Ghanem at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). She is currently an Assistant Professor in Computer Science at Prince Sultan University (PSU) in Riyadh, Saudi Arabia. Education and Career Sara received her Bachelor degree in Computer Science from Prince Sultan University (PSU) in Riyadh in 2010. She joined the MS/PhD program in KAUST and earned her Master and Ph.D. in Computer Science in 2017. She was a part of Image and Video Understanding

Reem Alghamdi

Ph.D. Student, Computer Science
Reem Alghamdi is a Ph.D student in VCCVIS group under the supervision of Professor Markus Hadwiger. Education Profile MS in Computer Science, King Abdullah University of Science and Technology, Saudi Arabia. B.Sc. in Computer Science, Princess Nourah bint Abdulrahman University, Saudi Arabia.

Peter Rautek

Research Scientist, Computer Science
Peter Rautek is a Research Scientist in the High-Performance Visualization Group working with Professor Markus Hadwiger at King Abdullah University of Science and Technology (KAUST).

Omar Mena Facio

M.S. Student, Computer Science
Omer Mena Facio is a Master student in Computer Science under the supervision of Professor Ivan Viola at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career Omer Mena Facio

Ngan Nguyen

Ph.D. Student, Computer Science
Ngan Nguyen is a Ph.D. candidate in the Computer Science Program under the supervision of Professor Ivan Viola in the Nanovisualization Research Group at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Ngan has a Master's Degree in Computer Science. She worked at Gameloft Company as a Developer. After that, she joined the University of Information Technology, Vietnam National University in Ho Chi Minh City as a Lecturer. Research Interest Ngan is focusing on Computer Graphics, Visualization, and their overlaps into

Neil Smith

Research Scientist, Computer Science
Neil Smith is a Research Scientist in the Computational Imaging Group (VCCIMAGING) working with Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Neil joined University of California in San Diego (UCSD) at the start his academic journey. He obtained his bachelor degree in Anthropology with Concentration in Archaeology in 2001. After that in 2003, he received his master degree in Anthropology. He has a doctoral in Anthropology since 2009. Dr. Smith started his career life as a Geographic Information Systems (GIS) Specialist at

Mohamed Ibrahim

Postdoctoral Research Fellow, Computer Science
Mohamed Ibrahim is a Postdoctoral Research Fellow in High-Performance Visualization Group (VCCVIS) working with Prof. Markus Hadwiger at King Abdullah University of Science and Technology (KAUST). Education and Early Career He earned his Bachelor’s degree in Computer Engineering from the American University in Cairo (AUC) in 2010. Later on, he obtained a Master’s degree in Computer Science from KAUST in 2012. He graduated from KAUST with a Ph.D. in Computer Science in 2019. Research Interest Mohamed Ibrahim's research is concerned with Large-Scale data visualization. He's especially interested

Matej Mlejnek

Research Scientist, Computer Science
Matej Mlejnek is a Research Scientist in the High Performance Visualization Group (HPV) working with Professor Markus Hadwiger at King Abdullah University of Science and Technology (KAUST). Education and Early Career He obtained his bachelor degree in Automation and Control from Slovak University of Technology in Slovakia in1999. After that, he joined Vienna University of Technology in Austria and received both his master (2003) and doctoral (2006) degrees in Computer Science. Research Interest Matej is interested in scientific visualization. Selected Publications Matej Mlejnek has many

Hisham Alhussain

Hisham Alhussain is a Computer Science Master Student under the supervision of Professor Markus Hadwiger at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Hisham received his bachelor's degree in Computer Science from the University of Massachusetts at Amherst in 2018. He is also a Software Developer at IBM. Research Interest He's currently focusing on Scientific Visualization and Visual Computing. He is proficient in Python, Java, and has professional experience in React/React-Native and mobile and web development

Haneen Mohammed

Haneen Mohammed was a Master student in Computer Science Program under the supervision of Professor Markus Hadwiger at Visual Computing Center (VCC) at King Abdullah university of Science and Technology (KAUST). Currently, she’s preparing her doctoral in Computer Science at Columbia University in the City of New York, United States. Education and Career Haneen earned her bachelor degree in Electrical and Computer Engineering from Effat University in Jeddah in 2015. She joined KAUST and received her master degree in Computer Science in 2017. Research Interest ​Her research interests are mainly

Eva Bones

Ph.D. Student, Computer Science
Eva Bones is a Ph.D. candidate in the Computer Science Program under the supervision of Professor Ivan Viola in the Nanovisualization Research Group at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Research Interest Eva is focusing on Computer Graphics and Visualization. Academic Advisors Ivan Viola, Professor, Computer Science.

Da Li

M.S. Student, Computer Science
Da Li is a Master student in Computer Science under the supervision of Professor Ivan Viola at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST).

Amani Ageeli

Ph.D. Student, Computer Science
Amani Ageeli is a Ph.D. student in the Computer Science program under the supervision of Professor Markus Hadwiger at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Amani received her bachelor's degree in Computer Science from Umm Al-Qura University in Makkah, Saudi Arabia in 2016. She joined KAUST to continue her studies and earned a master's degree in Computer Science in 2019. Currently, she's working on directed research with Prof. Markus Hadwiger in the High-Performance Visualization Group. Research Interest Amani

Alexandra Irger

M.S. Student, Computer Science
Alexandra Irger is a Master student in Computer Science under the supervision of Professor Ivan Viola at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career Before coming to KAUST, Alexandra graduated from TU Wien with a BSc in Media Informatics and Visual Computing in 2023. Research Interest Alexandra is focusing on Scientific Visualization and Computer Graphics.
<> IoT (15)
Syed Abidullah Fareed

Syed Abidullah Fareed

Visiting Student (former), Information Science Lab

Ahsan Mehmood

Intern (former), Information Science Lab
Visiting student at King Abdullah University of Science and Technology (KAUST).

Practical Cryptographic Framework to Manage Security and Privacy for IoT Endpoint devices

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B1 L4 R4214

In this talk, the speaker will provide a high-level introduction to his recent research on IoT endpoint security. Firstly, he will introduce requirements followed by a discussion on cryptographic algorithm implementation. He will mainly focus on an overview of efficient cryptography for IoT endpoints and system privacy issues. Then he will discuss security management approaches, positives, negatives and challenges to resolve, linking to the endpoint device security section with regards to realistic device needs/capabilities.

Paving the Way for Next Generation Wireless Networks

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B9 L2 H1 R2322

In this talk, I will introduce several ongoing projects at the networking lab in KAUST. I will start by highlighting our research profile along with our mission. Then, I will walk you through our projects and contributions in the domains of the internet of things, visible light communication, underwater communication, and future 6G networks. I will focus on the challenges facing each project, highlight our solution methodology, and discuss some performance evaluation results. I will focus on our work on Aqua-Fi, which aims at bringing the Internet into the underwater environment. I will also focus on our recent project on the communication via breath.

IoT via 5G and Beyond 5G Satellite Systems

Prof. Giovanni Giambene, Information Engineering and Mathematics, University of Siena, Italy

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KAUST

One of the key drivers for next-generation mobile communications is the support of the Internet of Things (IoT), with billions of objects connected to the Internet and very low latency. The 5G technology will support the realization of smart cities, smart environments, and big data applications.

Bridging the Gap between the Academia and Industry by Extending High-Quality Research to Practical Business Needs

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B9 L2 R2322 H1

This seminar focuses on providing the audience with the context and scope of our internship program. The program is for the young and talented graduate students with an active interest in solving real-world problems. Some of the projects that will be presented in the seminar are actively developed in Elm and include domains such as computer vision, robotics and automation, healthcare, IoT, video analytics, and NLP. The seminar will serve as a launch pad to allow students to discuss their future interests and aspirations with the speaker. It will also enable them to develop a better awareness of domains more relevant to their future research aspirations.

Scheduling tasks in energy harvesting IoT

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B4 L5 R5209

Internet of Things (IoT) applications can exploit energy harvesting systems to guarantee virtually uninterrupted operations. However, the use of energy harvesting poses issues concerning the optimization of the utility of the application while guaranteeing energy neutrality of the devices.

Towards Transparency of IoT Device Presence

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B9 L2 H2 H2

As many types of IoT devices worm their way into numerous settings and many aspects of our daily lives, awareness of their presence and functionality becomes a source of major concern. Hidden IoT devices can snoop (via sensing) on nearby unsuspecting users, and impact the environment where unaware users are present, via actuation.

KAUST Prospective Student: Siyuan Chen

1 min read ·
Siyuan Chen is a 22-year-old graduate who comes to KAUST from the School of Communication and Information Engineering, UESTC, China. Chen will join the University in the fall of 2020 as an M.S./Ph.D. candidate in the KAUST Structural and Functional Bioinformatics research group under the supervision of Professor Xin Gao.

Meet KAUST Prospective Student: Asaad AlGhamdi

1 min read ·
Asaad AlGhamdi is a 24-year-old graduate who will join KAUST in the fall from King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia. Asaad will join the University as a M.S. candidate and member of the Machine Intelligence and kNowledge Engineering research group under the supervision of Professor Xiangliang Zhang.

Meet KAUST Prospective Student: Xinge Yang

1 min read ·
Xinge Yang is a 22-year-old graduate who will join KAUST from the University of Science and Technology of China (USTC). Yang will join the University in the fall of 2020 as an M.S./Ph.D. candidate in the KAUST Computational Imaging Group under the supervision of Professor Wolfgang Heidrich.

Erick Rodrigues E Silva

Education Erick pursued his education in Control and Automation Engineering at CEFET-RJ, where he was awarded a Motion of Praise for his victory in a national robotics championship. He authored a thesis titled "Platform for Simulation and Generation of Synthetic Data for Autonomous Inspections by UAVS in Solar Plants." Experience During the thesis composition, Erick made a significant contribution to the paper "Virtual Reality Simulation of Autonomous Solar Plant Inspections with Unmanned Aerial Systems." This paper was published at ICUAS, a conference held in Dubrovnik, Croatia in 2021. After

Enas Ahmad

Enas Ahmad obtained her Master's degree in Computer Science under the supervision of Professor Basem Shihada at King Abdullah University of Science and Technology (KAUST). Research Interests Enas' research interests included the area of computer networking and systems, specifically software-defined networking, data centers, the internet of things, and participatory sensing. Thesis title: " Energy Efficient Smartphones: Minimizing the Energy Consumption of Smartphone's GPU using DVFS Governors". Education Profile M.sc. in Computer Science, King Abdullah University of Science and Technology

Charalambos (Harrys) Konstantinou

Professor Konstantinou is an expert in critical infrastructure security and resilience, focusing on smart grid technology, renewable energy integration and real-time simulation.

Asaad S. Alghamdi

<> bioscience (13)

Ali O Balubaid

Ph.D Directed Research at KAUST (King Abdullah University of Science and Technology)

Peak performance for proteins

1 min read ·
An improved peak fitting procedure enables a better determination of protein structures.

Disease researchers have a way with words

1 min read ·
Specialized word search method sheds light on phenotypic similarity of diseases.

Unlocking complex interactions

1 min read ·
Functions of interacting molecular complexes are uncovered through a novel method of aligning the interaction interface of each complex.

Noisy cells produce bursts of protein

1 min read ·
A new mathematical model explains how random factors affect the production of proteins within the cells.

Exploring artificial biosynthesis

1 min read ·
Metabolic route explorer helps to optimize the pathways for artificial biosynthesis of valuable products.

Driven to diffraction

1 min read ·
Optical elements that diffract light could lead to smaller and more powerful cameras.

Algorithm scours datasets to diagnose medical mysteries

1 min read ·
A new tool uses genetic and clinical information to find the root cause of unexplained illnesses.

Sharing expert experimental knowledge to expedite design

1 min read ·
A repository of metabolic information provides a quick reference tool for designing useful synthetic biological systems.

Computer models combat malaria

1 min read ·
A new malaria metabolic model may uncover better ways to treat a highly deadly disease.

Visualization of brain structures helps to model function

1 min read ·
Computer simulations and virtual reality are used by KAUST researchers and collaborators in France to visualize the energetic coupling between neurons and astrocytes and to improve understanding of brain metabolism. The human brain uses more energy than any other organ, accounting for around 20 percent of all glucose-derived energy. Neurons can’t meet their own energy requirements: they depend on supporting glial cells and the neurovascular system to supply various forms of sugar fuel. However, it is still unclear how this neuro-glio-vasculature network manages the brain’s energy demands

LD Motif Finder locates ancient hidden protein patterns

1 min read ·
A machine learning method has identified highly elusive amino acid sequences involved in cell morphogenesis and adhesion and in diseases like cancer.

Ricardo Henao

Associate Professor (former), Bioengineering
​Ricardo Henao, a quantitative scientist, is an Associate Professor of Bioengineering in the Biological and Environmental Science and Engineering ( BESE) Division, and Computer Science in the Computer, Electrical, Mathematical Sciences and Engineering Division ( CEMSE), member of the Smart Health Initiative ( SHI) and the Computational Biosciences Research center ( CBRC), at KAUST (King Abdullah University of Science and Technology). He is also currently an Associate Professor in the department of Biostatistics and Bioinformatics, Department of Electrical and Computer Engineering ( ECE)
<> algorithms (12)

PCCFD - Predictive Complex Computational Fluid Dynamics

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B9 L2 H1

The PCCFD workshop will focus on cutting-edge research in the field of algorithmic development for CFD and multi-scale complex flow simulations.

Efficient Machine Learning across Hardware, Software, and Algorithms

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B2 L5 R5209

Deep neural networks (DNNs) are revolutionizing computing, necessitating an integrated approach across the computing stack to optimize efficiency. In this talk, I will explore the frontier of DNN optimization, spanning algorithms, software, and hardware. We'll start with hardware-aware neural architecture search, demonstrating how tailoring DNN architectures to specific hardware can drastically enhance performance.

Machine learning models gather momentum

1 min read ·
New methods for training machine learning models are quicker and more accurate than current approaches, previously considered state-of-the-art.

Ferrofluid surface simulations go more than skin deep

1 min read ·
Computer models efficiently and accurately simulate the magnetic responses of ferrofluids by considering only the fluid’s surface.

Harnessing the particle-tracking power of the algorithm

1 min read ·
A simple camera system paired with a sophisticated image-processing algorithm can achieve faster and more accurate reconstructions of particle flow.

Ruogu Ding

Ruogu Ding obtained his Master's Degree in Computer Science under the supervision of Professor Panagiotis Kalnis at InfoCloud Research Group (CLOUD) in King Abdullah University of Science and Technology (KAUST). Prior to that, Ruogu received his Bachelor's degree in Computer Science from Peking University in July 2009. He worked as a research assistant on large-scale sequence assembly algorithms 2009-2010 at Peking University. Ruogu is currently serving as a Technology Partner at MiningLamp, a fast-growing startup aiming to provide enterprises with big data solutions. He worked on Algorithm

Peter Richtarik

Professor, Computer Science
Professor Richtárik specializes in big data optimization and machine learning. He is known for his work on randomized coordinate descent algorithms, stochastic gradient descent and federated learning.

Konstantin Mishchenko

Ph.D. Student, Computer Science
Konstantin Mishchenko is a Ph.D. candidate at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Richtarik in his research group. Education and Early Career Konstantin Mishchenko obtained his bachelor degree in Computer Science and Physics from Moscow Institute of Physics and Technology in 2016. After that in 2017, he received his master degree in Machine Learning MASH from Université Paris Dauphine in Paris, France. Konstantin had many internships during his studies. The most recent was with Amazon in

Elnur Gasanov

Elnur Gasanov is a master student at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Peter Richtárik in his research group. Education and Early Career Elnur Gasanov obtained his bachelor degree in Applied Mathematics and Physics from Moscow Institute of Physics and Technology (MIPT) in Moscow, Russia in 2018. Research Interest His research interests are in optimization and machine learning. Education Profile ​B.Sc., Applied Mathematics and Physics, Moscow Institute of Physics and Technology (State

Amin Allam

Amin Allam obtained his Ph.D. in Computer Science at the King Abdullah University of Science and Technology (KAUST) under the supervision of Prof. Panagiotis Kalnis in 2017. Amin is a lecturer in the Computer Science department, Faculty of Computers and Information, Cairo University, Egypt. He got his bachelor's degree in Computers and Information in 2003 from Cairo University. Then, he got a Master's degree titled "An Arabic-Disambiguating Engine Based on Statistical Approaches" in Computer Science from the same university in 2008. Research Interest Amin's research interests include

Ali Charara

Ali Charara obtained both, his MS Degree and PhD Degree from Computer Science at KAUST in 2011 and 2018 respectively. His master thesis supervisor was Alyn Rockwood and his PhD thesis supervisor was David Keyes. Ali's research interests were in High Performance Computing with a focus on parallel computing and algorithms using multicore and manycore Computer Graphics Units. Education Profile MS Degree, Computer Science, KAUST (2011) PhD Degree, Computer Science, KAUST (2018)

Ahmed El-Roby

Ahmed El-Roby obtained his M.S. degree in Computer Science under the supervision of Professor Panagiotis Kalnis at InfoCloud Research Group (CLOUD) in King Abdullah University of Science and Technology (KAUST). He got his Ph.D. in Computer Science from the David R. Cheriton School of Computer Science at the University of Waterloo. Currently, Ahmed is working as an Assistant Professor at the School of Computer Science at Carleton University in Ottawa, Ontario. Research Interests Ahmed's research interests included Data Integration, Automatic Linking, User Feedback, Artificial Intelligence, and
<> applied mathematics (12)

Peter Schmid

​Professor Schmid's research interests are in theoretical and computational fluid dynamics, with emphasis on hydrodynamic stability theory, flow control, model reduction and system identification. He is also interested in computational techniques for flow optimization and quantitative flow analysis.

PCCFD - Predictive Complex Computational Fluid Dynamics

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B9 L2 H1

The PCCFD workshop will focus on cutting-edge research in the field of algorithmic development for CFD and multi-scale complex flow simulations.

Making the silent genes speak

1 min read ·
Regulating an overlooked set of genes may help biologists manipulate cells to find new therapy options.

Taking computing to the next level

1 min read ·
KAUST’s new supercomputer will enable ground-breaking scientific modeling and analysis in Saudi Arabia and worldwide.

Finding the Face in the Crowd

1 min read ·
An algorithm that maximizes the difference between data categories achieves high accuracy in classifying faces.

Honey bee behavior is in the genes

1 min read ·
An integrated set of genetic mechanisms controls whether bees behave as foragers or nurses.

Enhancing understanding of genetic regulation

1 min read ·
Researchers round up clues to track down enhancers.

Filling in the blanks of virtual cities

1 min read ·
New software generates three-dimensional models of buildings, with applications for disaster relief support.

Taking graphics cards beyond gaming

1 min read ·
A highly efficient mathematical solver designed to run on graphics processors gives scientists and engineers a powerful new tool for a common computational problem.

Signaling success for 5th gen communications

1 min read ·
An efficient wireless signal optimization scheme achieves peak performance for next-generation mobile communications.

Cutting datasets down to size

1 min read ·
A powerful statistical tool could significantly reduce the burden of analyzing very large datasets.

Keep the data coming

1 min read ·
A continuous data supply ensures data-intensive simulations can run at maximum speed.
<> computational science (12)

KAUST Research Conference Digital Health 2020

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B19 L2 H1

Computational Bioscience Research Center at King Abdullah University of Science and Technology is pleased to announce the KAUST Research Conference on Digital Health 2020.

Making the silent genes speak

1 min read ·
Regulating an overlooked set of genes may help biologists manipulate cells to find new therapy options.

Taking computing to the next level

1 min read ·
KAUST’s new supercomputer will enable ground-breaking scientific modeling and analysis in Saudi Arabia and worldwide.

Finding the Face in the Crowd

1 min read ·
An algorithm that maximizes the difference between data categories achieves high accuracy in classifying faces.

Choosing the right scheme for green communications

1 min read ·
Performance analysis reveals how different resource allocation schemes may impact the energy efficiency of a cognitive radio system.

Sharing movement aids learning

1 min read ·
Automated learning of an individual’s movement patterns shared over mobile and social networks could help us to connect better.

Enhancing understanding of genetic regulation

1 min read ·
Researchers round up clues to track down enhancers.

Filling in the blanks of virtual cities

1 min read ·
New software generates three-dimensional models of buildings, with applications for disaster relief support.

Technology search for relationships

1 min read ·
A sketch-based query for searching for relationships among objects in images could enhance the power and utility of image search tools.

Taking graphics cards beyond gaming

1 min read ·
A highly efficient mathematical solver designed to run on graphics processors gives scientists and engineers a powerful new tool for a common computational problem.

Cutting datasets down to size

1 min read ·
A powerful statistical tool could significantly reduce the burden of analyzing very large datasets.

Keep the data coming

1 min read ·
A continuous data supply ensures data-intensive simulations can run at maximum speed.
<> data analysis (12)

Nizar Ajeeb

Full Time Researcher Full at King Abdullah University of Science and Technology.

Computational Methods for ChIP-seq Data Analysis and Applications

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B3 L5 5209

Abstract The development of Chromatin immunoprecipitation followed by sequencing (ChIP-seq) technology has enabled the construction of genome-wide maps of protein-DNA interaction. Such maps provide information about transcriptional regulation at the epigenetic level (histone modifications and histone variants) and at the level of transcription factor (TF) activity. This dissertation presents novel computational methods for ChIP-seq data analysis and applications. The work of this dissertation addresses four main challenges. First, I address the problem of detecting histone modifications from

Ontology Design Patterns for Combining Pathology and Anatomy: Application to Study Ageing and Longevity in Inbred Mouse Strains

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B9 R3120

Abstract In biomedical research, ontologies are widely used to represent knowledge as well as annotate datasets. Many of the existing ontologies cover a single type of phenomena, such as a process, cell type, gene, pathological entity or anatomical structure. Consequently, it is required to use multiple ontologies to fully characterize the observations in the datasets. Although this allows precise annotation of different aspects of a given dataset, it limits our ability to use the ontologies in data analysis, as the ontologies are usually disconnected and their combination cannot be exploited

AI4GH Seminar Series - Vector Representation of Biological Entities Based on Ontologies and Their Annotations

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B2 R5220

Biological knowledge is widely represented in the form of ontology-based annotations: ontologies describe the phenomena assumed to exist within a domain, and the annotations associate a biological entity with a set of phenomena within the domain.

Neuro-symbolic systems in the life sciences

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B9 H1 R2322

Abstract The life sciences have invested significant resources in the development and application of semantic technologies to make research data accessible and interlinked, and to enable the integration and analysis of data. Utilizing the semantics associated with research data in data analysis approaches is often challenging. Now, novel methods are becoming available that combine symbolic methods and statistical methods in Artificial Intelligence. In my talk, I will describe how to generate knowledge graph embeddings for analysis of biological and biomedical data. Brief Biography Robert

Student life: studying and working at the Computational Bioscience Research Center (KAUST)

1 min read ·
For Ph.D. students Arturo Magana Mora, Haitham Ashoor and Olaa Motwalli, April 2017 was a particularly nerve-wracking month. After years of hard work and sleepless nights, they finally defended their thesis, bringing them one step closer to graduation.

Superior seismic imaging for better drilling

1 min read ·
Software could transform underground imaging of fossil fuel reserves by providing unprecedented detail in record time.

Yang Liu

Ph.D. Student, Computer Science
Yang Liu is an M.S./Ph.D. candidate in the KAUST Bio-Ontology research group under the supervision of Professor Robert Hoehndorf. Before joining KAUST. Research Interests Yang's interests include bioinformatics, machine learning and human genomics. She is interested in building models for human disease diagnosis and prognosis, especially for cancer, and applying statistical methods to analyze human omics data. Education Profile B.Sc, Bioengineering, Harbin Institute of Technology, Harbin, China, 2016-2020. M.Sc, Bioengineering, King Abdullah University of Science and Technology, Thuwal, Saudi

Vasiliki Kordopati

Vasiliki Kordopati obtained Master degree in Computer Science under the co-supervision of Professor Boon Ooi at Photonics Laboratory and Prof. Xin Gao at Structural and Functional Bioinformatics Group (SFB) from King Abdullah University of Science and Technology (KAUST). Vasiliki is a co-founder of Oæsis, a King Abdullah University of Science and Technology (KAUST) startup spinout, where she is responsible for product development. A computer engineer by training, she received a bachelor's degree in Computer Engineering from the University of Patras, Greece. Research Interest Vasiliki's

Ronell Sicat

Research Scientist, Computer Science
Ronell Sicat is a Research Scientist in the High Performance Visualization Group working with Professor Markus Hadwiger at King Abdullah University of Science and Technology (KAUST). His research interests are in large-scale data visualization and analysis, immersive analytics, and computer graphics.

Haitham M. Ashoor

Research Interests Haitham Ashoor is a Ph.D. candidate in the Computer Science program at King Abdullah University of Science and Technology (KAUST). He is working under the supervision of Prof. Vladimir Bajic. He has obtained his BSc degree in Computer Engineering from the University of Jordan, Amman Jordan in 2008. He joined KAUST as an MSc student in 2009. In 2011, He finished his master studies under the supervision of Prof. Vladimir Bajic. His main research interests are developing computational methods for next-generation sequencing data analysis and applications of machine learning in

Francesco Napolitano

​Francesco is a Computer Scientist with a background in Mathematical Models and Machine Learning. He focused his M.Sc. (University of Salerno, SA, Italy) and Ph.D. (University of Salerno and the University of California, Irvine, CA, USA) on supervised and unsupervised data analysis, particularly for clustering of complex, high-dimensional data. He developed and applied data analysis techniques to problems from different fields, including fault detection in avionics and computational pharmacology, before focusing on bioinformatics and systems biology. Research Interests Francesco's main
<> distributed systems (11)

Useful Bitcoin Mining with a Matrix-based Puzzle

Cryptocurrency and blockchain technologies are increasingly gaining adoption since the introduction of Bitcoin, being distributed, authority-free, and secure. Proof of Work (PoW) is at the heart of blockchain’s security, asset generation, and maintenance. In most cryptocurrencies, and mainly Bitcoin, the “work” a miner must do is to solve a cryptographic puzzle: to find a random nonce that once (cryptographically) SHA-256 hashed with a perspective block header, returns a 32 Bytes number having a leading pre-defined number of zeros (called difficulty). This puzzle represents the PoW, and lives

Breaking the Vehicle Over-The-Air Update System

A modern vehicle is composed of around 100 Electronic Control Unit (ECU) connected via several types of networks. An ECU is an embedded device, similar to a RaspberryPI, running an operating system, e.g., Linux-based or real-time OS, on top of which different software and firmware may run, depending on the application. Due to the imperfection of humans, software can have faults and vulnerabilities, which can lead to catastrophic failures that threatens human lives. This makes the manufacturers liable to such failures and thus often caused millions of vehicles recalls for repair. A smart

An Overview of Systems Research at TU Munich

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B4 L5 R5209

In this talk, Professor Pramod Bhatotia will give an overview of systems research at TU Munich. He will cover his teaching and ongoing research projects. And will conclude the talk with a brief overview of his ERC project.

Cocoon finally out: Professor Marco Canini together with VMware, Samsung, and Microsoft craft the ultimate SDN ally

1 min read ·
With the recent emergence of software-defined networking, which brings programmability and lowers the barrier for new functionalities into networks, the academic and industry communities have become very interested in the problem of network verification.

Yousif Aldolaijan

Yousif Aldolaijan was a Computer Science Master student under the supervision of Prof. Ivan Viola at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Yousif received his bachelor’s degree in Computer Science at the University of Southern California (USC), Los Angeles, USA in 2018. He joined KAUST to continue his studies in Computer Science and received his Master's degree in Computer Science from KAUST in 2020. During the summer of 2018, he joined Google as a Software Engineering Intern for three months. He worked on

Marco Canini

Associate Professor, Computer Science
Professor Canini’s research seeks to improve networked-system design, implementation and operation concerning vital properties such as reliability, performance, security and energy efficiency.

Ibrahim Abdelaziz

Ibrahim Abdelaziz obtained his Ph.D. degree in Computer Science under the supervision of Professor Panagiotis Kalnis from King Abdullah University of Science and Technology (KAUST). Research Interests Ibrahim Abdelaziz's research interests included Data Mining over large datasets, Distributed Systems, Machine Learning and Pattern Recognition. Selected Publications Using Advanced Hidden Markov Models for online handwriting recognition. Ibrahim Hosny, Sherif Abdou, Aly Fahmy In Proceeding of Asian Conference of Pattern Recognition (ACPR), (2011) Large Vocabulary Arabic Online Handwriting

Elmootazbellah Elnozahy

Professor (former), Computer Science
Elmootazbellah (Mootaz) Elnozahy is a renowned computer scientist who has filed, individually and with colleagues, 58 U.S. patents. He has successfully secured research and development funding grants of ~$320M.

Asaad S. Alghamdi

Arnaud Dethise

Ph.D. Student, Computer Science
Arnaud Dethise is a PhD candidate in Computer Science under the guidance of Professor Marco Canini. He completed his Bachelor's degree in Engineering and Master's degree in Computer Science in 2015 and 2017 respectively, from UCLouvain, Louvain-la-Neuve, Belgium. Arnaud has a strong interest in machine learning applied to distributed systems and is currently pursuing his doctoral research in this field. His specific research interests include Neural Networks, trustworthiness, verifiability and explainability, and model privacy, with a focus on exactness and provable guarantees. Arnaud’s goals

Ali Shoker

Senior Research Scientist, Computer Science
I am a Senior Staff Scientist, working on Cyber Security and Resilience. My research is focused on studying and using Byzantine and Intrusion Fault tolerance, Hardware (FPGA), Artificial Intelligence, and Blockchain, for critical applications like Autonomous Vehicles, Small Satellite constellations, SOC, and Fintech. I love and plan to build practical things that manifest in the society.
<> supercomputing (11)

Useful Bitcoin Mining with a Matrix-based Puzzle

Cryptocurrency and blockchain technologies are increasingly gaining adoption since the introduction of Bitcoin, being distributed, authority-free, and secure. Proof of Work (PoW) is at the heart of blockchain’s security, asset generation, and maintenance. In most cryptocurrencies, and mainly Bitcoin, the “work” a miner must do is to solve a cryptographic puzzle: to find a random nonce that once (cryptographically) SHA-256 hashed with a perspective block header, returns a 32 Bytes number having a leading pre-defined number of zeros (called difficulty). This puzzle represents the PoW, and lives

Fifth KAUST-NVIDIA Workshop on Accelerating Scientific Applications Using GPUs

Timothy Lanfear , Brent Leback

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B4 B5 A0215

The KAUST Supercomputing Laboratory is co-organizing with NVIDIA, a leader in accelerated computing and artificial intelligence, a full-day workshop on accelerating scientific applications using GPUs on Tuesday, February 20th, 2018 in the auditorium between buildings 4 and 5.

Taking computing to the next level

1 min read ·
KAUST’s new supercomputer will enable ground-breaking scientific modeling and analysis in Saudi Arabia and worldwide.

CEMSE Big Data Open Day shows off fascinating discoveries

1 min read ·
The fifth-anniversary event marked CEMSE's biggest successes and featured 20 KAUST discoveries, with more than 1,000 visitors from the community attending the event."Big data has many applications: to understand medicine better; to manage food supplies, and to connect objects. Data is at the center of everything," said Dean Mootaz Elnozahy of the University's Computer, Electrical, Mathematical Science and Engineering (CEMSE) Division at the CEMSE Big Data Open Day held on December 4, 2016.

New Data Mining approach makes recurring patterns easier to be spotted

1 min read ·
The ultimate answer is ScaleMine, cost-effective and agile scalable parallel frequent subgraph mining in a single large graph. Panagiotis Kalnis, Professor of Computer Science (CS), and the team from the KAUST Extreme Computing Research Center (ECRC), under the Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division signed this novel approach.

KAUST gets ready for SC18

1 min read ·
For the past 10 years, KAUST has made a strong presence at the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC) that takes place in different cities around the U.S. every year. 2018 will mark a special year for KAUST at SC, which will take place in Dallas, Texas, in November, with around 13,000 supercomputing professionals in attendance. The University will have an engaging presence on the exhibition floor and in the seminars, and the technical program of SC18 will be chaired by the Director of the KAUST Extreme Computing Research Center (ECRC)

KAUST Professor David Keyes to chair International Supercomputing Conference 2020

1 min read ·
David Keyes, KAUST professor of applied mathematics and computational science; director of the University's Extreme Computing Research Center; and senior associate to the President of KAUST, will chair the International Supercomputing Conference (ISC) 2020. He will be the first program chair from a Middle Eastern institution, as announced by ISC 2020.

Shaheen II open to serve COVID-19 research around the Kingdom

1 min read ·
The COVID-19 pandemic has imposed challenges across the globe that need to be solved using scientific discovery and innovation. In line with that, the KAUST Supercomputing Core Laboratory (KSL) has opened a call for proposals that require its supercomputer Shaheen II, as well as other cluster computing resources, to support research projects.

KAUST scientists win prestigious Gauss Award for supercomputing excellence

1 min read ·
KAUST Ph.D. graduate Dr. Noha Al-Harthi and doctoral student Rabab Alomairy, have won the German Gauss Center for Supercomputing (GCS) Award for original research that best advances high-performance computing. This makes KAUST the first Middle Eastern institution to receive this prestigious award.

KAUST presents exascale expertise at SC21

1 min read ·
Supercomputing experts from KAUST converged with global participants at the America's Center in St. Louis, Missouri from November 14 to 19 for SC21, the annual international conference for high performance computing (HPC), networking, storage and analysis. Established in 1988, the SC conference is the largest HPC event of its kind, with big name industries like Intel, Red Hat and Oracle sharing the exhibition floor alongside universities, government agencies and small businesses, and professionals from various affiliations attending presentations throughout the convention center. In non
kaust-cemse-panagotis-kalnis

Panagiotis Kalnis

Professor, Computer Science
Professor Panos Kalnis is a leading researcher in big data, cloud computing, parallel and distributed systems and computing privacy for data mining and bioinformatics.
<> data integration (10)

AI4GH Seminar Series | Predicting Protein Function and Phenotype Associations

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B2 R5220

The amount of available protein sequences is rapidly increasing, mainly as a consequence of the development and application of high ​throughput sequencing technologies in the life sciences.

Novel Computational Methods to Predict Drug–target Interactions Using Graph Mining and Machine Learning Approaches

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B3 L5 R5220

Abstract Computational drug repurposing aims at finding new medical uses for existing drugs. The identification of novel drug-target interactions (DTIs) can be a useful part of such a task. Finding computationally DTIs is a convenient strategy to identify potentially new DTIs at low cost with reasonable accuracy. However, the current DTI prediction methods suffer a high false positive prediction rate. Here, we present a comprehensive review of the recent progress in the field of DTI prediction from data-centric and algorithmic-centric perspectives that can help in constructing novel reliable

AI4GH Seminar Series - Machine Learning with Biological Knowledge Graphs

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B2 R5220

Abstract Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval, and federated queries. In the past years, feature learning methods that are applicable to graph-structured data are becoming available, but have not yet widely been applied and evaluated on structured biological knowledge. We develop novel methods for feature learning on biological knowledge graphs and apply them to the prediction of edges in the knowledge graph representing problems of finding candidate genes of diseases, or drugs

Mona Alshahrani defends her thesis proposal

1 min read ·
Mona Alshahrani, a Ph.D. candidate under the supervision of Assistant Professor Robert Hoehndorf, defended her thesis proposal on October 10, 2018.

Senay Kafkas

Research Scientist, Bio-Ontology Research Group
Research Interests Senay's research interests include biomedical text/data mining, Natural Language Processing (NLP), and Next Generation Sequencing (NGS) data analysis. Scientific and Professional Membership ​International Society of Computational Biology (ISCB) (2010-present). KAUST Affiliations Bio-Ontology Research Group (BORG).​ Computational Bioscience Research Center (CBRC). Computer, Electrical, and Mathematical Sciences and Engineering (CEMSE). Education Profile Ph.D., Computer Engineering, Eastern Mediterranean University, Cyprus, 2012. M.Sc., Computer Engineering, Eastern

Rawan Olayan

Rawan Olayan obtained both, her MS Degree and PhD Degree from Computer Science at KAUST in 2013 and 2018 respectively. Her master thesis and PhD thesis supervisor was Vladimir Bajic. Under the supervision of her advisor Professor Vladimir Bajic, she completed her thesis on developing novel computational methods to predict drug-target interactions and their functional effects using data integration, graph mining and machine learning approaches. Currently, Rawan is working as a postdoctoral associate in the Carter Lab at the Jackson Laboratory for Genomic Medicine in Farmington, Connecticut. In

Mona Alshahrani

Mona Alshahrani obtained her Ph.D. degree in Computer Science under the supervision of Professor Robert Hoehndorf at the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST). Research Interests Machine Learning, human bioinformatics, data integration. The combination of neuro-symbolic methods over ontologies and semantic RDF graphs with statistical methods. Their applications in data analysis and features representations in several biomedical applications such as phenotypes prediction, drug repurposing, and others. I'm also interested in network

Alyaa M Mohamed

Ph.D., Bioscience
Alyaa M Mohamed obtained her Ph.D. degree under the co-supervision of Prof. Xin Gao at Structural and Functional Bioinformatics Group and Prof. Takashi Gojobori at King Abdullah University of Science and Technology (KAUST). Currently, Alyaa is a Postdoc Fellow at University of Calgary, Canada. Research Interests Genome-scale metabolic modeling, Gene expression analysis, Constraint-based modeling. Education Profile Ph.D. in Bioscience, King Abdullah University of Science and Technology, Saudi Arabia, 2012-2018. M.Sc. in Pharmacology, German University in Cairo, Egypt, 2010. ​B.Sc. in Pharmacy

Ahmed El-Roby

Ahmed El-Roby obtained his M.S. degree in Computer Science under the supervision of Professor Panagiotis Kalnis at InfoCloud Research Group (CLOUD) in King Abdullah University of Science and Technology (KAUST). He got his Ph.D. in Computer Science from the David R. Cheriton School of Computer Science at the University of Waterloo. Currently, Ahmed is working as an Assistant Professor at the School of Computer Science at Carleton University in Ottawa, Ontario. Research Interests Ahmed's research interests included Data Integration, Automatic Linking, User Feedback, Artificial Intelligence, and

Adil Salhi

Adil Salhi is a Research Engineer at Prof. Xin Gao's Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Adil obtained his Ph.D. degree in Computer Science from the University of Manchester, UK. Research Interests Adil's research interests lie in the area of data and text mining of biomedical literature. The extraction of new information from disparate structured data sources, or available information buried in free-form text, requires the deployment of a wide array of tools to overcome the challenges posed by the nature and size
<> Federated learning (10)

EF-BV: distributed optimization with compressed communication

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B9 L2 R2322 H1

In the big data era, it is necessary to rely on distributed computing. For distributed optimization and learning tasks, in particular in the modern paradigm of federated learning, specific challenges arise, such as decentralized data storage. Communication between the parallel machines and the orchestrating distant server is necessary but slow. To address this main bottleneck, a natural strategy is to compress the communicated vectors. I will present EF-BV, a new algorithm which converges linearly to an exact solution, with a large class of deterministic or random, biased or unbiased compressors.

Making federated learning practical and confidential

Dr. Ruichuan Chen, Distinguished Member of Technical Staff and a Tech Lead, Nokia Bell Labs

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B4 L5 R5209

Federated learning (FL) is increasingly deployed among multiple clients to train a shared model over decentralized data. To address the privacy concerns, FL systems need to protect the clients' data from being revealed during training, and also control data leakage through trained models when exposed to untrusted domains. However, existing FL systems (with distributed differential privacy) work impractically in the presence of client dropout, resulting in either poor privacy guarantees or degraded training accuracy. In addition, existing FL systems focus on safeguarding the privacy of training data, but not on protecting the confidentiality of the models being trained, which are increasingly of high business value. In this talk, I will present two pieces of our recent work that aim to address these aforementioned issues.

FADO: A Federated Learning Attack and Defense Orchestrator

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B5 L5 R5209

Federated Learning (FL) is a distributed machine learning approach that allows multiple parties to train a model collaboratively without sharing sensitive data.

Resistance is Futile: Balancing Opportunities and Security Challenges of Federated Learning

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B4 L5 R5220

The rapid growth of Artificial Intelligence (AI) and Deep Learning mirrors an infectious phenomenon. While AI systems promise diverse applications and benefits, they bear substantial security and privacy risks. Indeed, AI represents a goldmine for the security and privacy research domain.

Ziwu Liu

M.S. Student, Computer Science
My passion for the evolving landscape of technology has driven me to focus on the intricate interplay between resilient computing, federated learning, edge computing, and machine learning systems. My research aims to seamlessly integrate edge computing with federated learning to create resilient and adaptive machine learning systems that can operate effectively even in resource-constrained settings.

Norah S. Alballa

Ph.D. Student, Computer Science
Education Profile Ph.D. in Computer Science, CEMSE, KAUST, 2022-present M.S. in Computer Science, Portland State University, 2016 B.S. in Information Technology, King Saud University (KSU), 2010

Laurent Condat

I am a senior researcher working in optimization, in the group of Prof. Peter Richtárik.

Konstantin Burlachenko

Ph.D. Student, Computer Science
Konstantin Burlachenko is a Ph.D. candidate at the KAUST Optimization and Machine Learning Lab under the supervision of Professor Peter Richtarik.

Egor Shulgin

Ph.D. Student, Computer Science
Egor Shulgin is a CS student in the MS/Ph.D. program under the supervision of Professor Peter Richtarik at the Visual Computing Center (VCC) at the King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Education and Early Career Egor obtained his bachelor's degree in Applied Mathematics, Physics, and Computer Science from the Moscow Institute of Physics and Technology (MIPT) in 2019. He first came to KAUST for a research visit in 2019. Later, he joined Prof. Peter Richtarik's group as a MS/Ph.D. student. In 2021 Egor did a research internship with Distributed AI team at the

Boris Radovic

Boris Radovic is a Ph.D. candidate in the computer science program, under the supervision of Professors Marco Canini and Veljko Pejović, and is a member of the Software-defined Advanced Networked and Distributed Systems (SANDS) Lab. He holds a Bachelor of Science (B.Sc.) in Computer Science and a Master of Science (M.Sc.) in Data Science, both obtained from the Faculty of Computer and Information Science at the University of Ljubljana, Slovenia. During his academic journey, Boris gained experience across diverse computer science domains through his long-term collaboration with the Laboratory
<> geometry processing (10)

Workshop on Applied Geometry and Visual Computing

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B4 L5 R5209

The “KAUST Workshop on Applied Geometry and Visual Computing” brings together leading scientists from Europe and the United States, presenting their latest results in - Applied and Discrete Differential Geometry - Geometry Processing - Computational Fabrication The talks are related to various problems in Applied Mathematics in general and to further areas of Visual Computing such as Computer Graphics, Physical Simulation and Scientific Visualization. The workshop provides a great opportunity to learn about latest developments and to discuss ongoing work with top researchers in the field.

Robust Geometry Processing for Physical Simulation

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B4 L5 R5209

I will discuss the limitations of the current state of the art, and present a proposal for an integrated pipeline, considering data acquisition, meshing, basis design, and numerical optimization as a single challenge, where tradeoffs can be made between different phases to increase automation and efficiency. I will demonstrate that this integrated approach offers many advantages, while opening exciting new geometry processing challenges, and that a fully opaque meshing and analysis solution is already possible for heat transfer and elasticity problems with contact. I will present a set of applications enabled by this approach in reinforcement learning for robotics, force measurements in biology, shape design in mechanical engineering, stress estimation in biomechanics, and simulation of deformable objects in graphics.

Cohomology in fluids

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B4 L5 R5209

We present a topological analysis of the vorticity formulation in describing fluid dynamics. Despite its widespread use in fluid mechanics, this formulation is insufficient at describing fluid dynamics on a non-simply-connected domain. What is missing is an equation of motion for fluid's cohomology component, which exhibits fascinating dynamics previously under explored. Using geometric language, we derive the new equation of motion and establish new conservation laws, as Casimir invariants in Hamiltonian mechanics, for fluids on domains with general topology. Significantly, we present the first physically correct vortex method on curved surfaces with genus and boundaries.

Discrete Surfaces from Developable Strips

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B4 L5 R5209

We will discuss several settings in which smooth developable strips are attached to each other or assembled in some other way to obtain surfaces with interesting geometric properties. Driven by the view towards applications we will investigate collections of strips which lie orthogonal or tangential to a reference surface and assume particular shapes. Such strips can serve as support structures or cladding panels of free-form shapes in architectural contexts. Our focus will lie on surfaces with a constant ratio of principal curvatures, cone nets, geodesic grid shells, and others.

Yipeng Qin

Postdoctoral Research Fellow, Computer Science
Yipeng Qin was a Postdoctoral Research Fellow working with Professor Peter Wonka at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Currently, he is a Lecturer at the School of Computer Science and Informatics in Cardiff University (CU), United Kingdom. Education and Early Career In 2013, Yipeng Qin graduated with a Bachelor of Engineering in Electronic Engineering from Shanghai Jiao Tong University in China. Later during 2017, he obtained his Ph.D. in Computer Science from the National Centre for Computer Animation, Bournemouth University in

Victor Ceballos Inza

Ph.D. Student, Computer Science
Victor Ceballos Inza is a Ph.D. student under the supervision of Prof. Helmut Pottmann at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST).

Michael Birsak

Research Scientist, Computer Science
Michael Birsak is a Research Scientist working with Professor Peter Wonka at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST).

Jing Ren

Ph.D. Student, Computer Science
Jing Ren is a Ph. D. candidate under the supervision of Prof. Peter Wonka at King Abdullah University of Science and Technology (KAUST). Education and Early Career Jing Ren obtained a Bachelor’s Degree in Mathematics and Applied Mathematics from Zhejiang University in China. Later on in 2015, she received her M.Sc. degree in Mathematical and Computational Finance from the University of Oxford in United Kingdom. Scientific Interest Jing Ren is mainly interested in her research in Computer Graphics and Geometry Processing. Selected Publications One of the recent publication that Jing Ren co

Jens Schneider

Research Scientist, Computer Science
Jens Schneider was a Staff Scientist at the Visual Computing Center in King Abdullah University of Science and Technology (KAUST). Currently, he’s an Assistant Professor in College of Science and Engineering, Hamad Bin Khalifa University (HBKU), Qatar. Education and Early Career Jens Schneider received a diploma equivalent to Master degree in Computer Science from RWTH Aachen in Germany, 2004. He earned his Ph.D. in Computer Science from Technical University Munich, Germany, in 2009 Dr. Schneider joined KAUST in 2009 as a Postdoctoral Research Fellow. Later on, he was promoted to Research

Caigui Jiang

Research Scientist, Computer Science
Caigui Jiang is a Research Scientist at the Visual Computing Center (VCC) working with Professor Helmut Pottmann at King Abdullah University of Science and Technology (KAUST). Education and Early Career Dr. Jiang obtained a bachelor degree in Electronic Engineering and his masters in Pattern Recognition and Intelligent Systems from Xi'an Jiaotong University (XJTU) in China. After that, he joined KAUST to receive his Ph.D. in Computer Science in 2016. Caigui has a diverse experience in the academic and research field. In 2016, he joined Max Planck Institute for Informatics as a Postdoctoral
<> human health (10)

AI4GH Seminar Series - Machine Learning with Biological Knowledge Graphs

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B2 R5220

Abstract Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval, and federated queries. In the past years, feature learning methods that are applicable to graph-structured data are becoming available, but have not yet widely been applied and evaluated on structured biological knowledge. We develop novel methods for feature learning on biological knowledge graphs and apply them to the prediction of edges in the knowledge graph representing problems of finding candidate genes of diseases, or drugs

Professors discuss big data and human health in latest Sci-Café

1 min read ·
​Professors Takashi Gojobori, Xin Gao and Robert Hoehndorf spoke about big data and human health at the Sci-Café event held on February 27. Their talk, which was moderated by James Calvin, Vice President for Academic Affairs, aimed to answer the question, "Can Big Data Solve My Health Problems?" This event was open to all members of the KAUST community.

Computer models combat malaria

1 min read ·
A new malaria metabolic model may uncover better ways to treat a highly deadly disease.

Database to support infectious disease research

1 min read ·
Linking disease pathogens to clinical signs and symptoms through a database could support research into the molecular mechanisms of infectious diseases.

LD Motif Finder locates ancient hidden protein patterns

1 min read ·
A machine learning method has identified highly elusive amino acid sequences involved in cell morphogenesis and adhesion and in diseases like cancer.

Algorithm turns cancer gene discovery on its head

1 min read ·
A method for finding genes that spur tumor growth takes advantage of machine learning algorithms to sift through reams of molecular data collected from studies of cancer cell lines, mouse models and human patients.

Studying COVID-19’s envelope protein

1 min read ·
A likeness between genes of the SARS and COVID-19 viruses could inform research into potential treatments.

Viruses as communication molecules

1 min read ·
Electrical and computer engineers take on complex modeling questions that can further our understanding of virus spread in small spaces.

New database to "SpUR" on cancer research

1 min read ·
Xin Gao's team developed an interactive web portal where cancer scientists can interrogate how RNA splicing in noncoding parts of genes fuels the growth of different types of tumors.

AI helps diagnose post-COVID lung problems

1 min read ·
A new AI diagnostic tool developed by KAUST scientists allows doctors to visualize lung damage caused by COVID-19 in more detail.
<> modeling (10)

Houcem Ben Salem

Intern (former), Information Science Lab
Research Intern at KAUST(King Abdullah University of Science and Technology) .

Machine Learning Models for Context Aware Recommendation Systems

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B3 L5 R5209

The goal of this thesis is to pave the way towards the next generation of recommendation systems tackling such real-world challenges to improve the user experience while giving good recommendations.

Eliminating Waiting Time Inequities for Deceased Donor Kidney Transplantation in the USA

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B9 L2 H1 R2322

This research formally models, analyzes and maximizes equity of transplant waiting times and probabilities using queuing theory and network flows, based on Rawls' theory of justice. The presented formal models address inequities resulting from blood type incompatibilities, which are interrelated to ethnic differences in patient and donor rates.

KAUST Ph.D. student Jinhui Xiong wins best paper award

1 min read ·
KAUST computer science Ph.D. student Jinhui Xiong recently won the best paper award at the 24th International Symposium on Vision, Modeling, and Visualization. The symposium took place from September 30 to October 2, 2019, at the University of Rostock, Germany, and provided the opportunity for researchers to discuss a wide range of topics in computer science, including computer graphics, vision, visualization and visual analytics.

Training an AI eye on the Moon

1 min read ·
Machine learning accelerates the search for promising Moon sites for energy and mineral resources.

Viruses as communication molecules

1 min read ·
Electrical and computer engineers take on complex modeling questions that can further our understanding of virus spread in small spaces.

Wildfire simulation heats up

1 min read ·
Computational models capture the capricious behavior of forest fires.

Watching viscous flow, but faster

1 min read ·
A state-of-the-art method for modeling the behavior of liquids described by the Computational Sciences Group represents a breakthrough in computational speed for viscous liquids.

A model for millions of locations

1 min read ·
CEMSE statisticians developed a framework which enables modeling of a range of meteorological and environmental datasets from up to 2 million locations globally.

Ivan Viola

Professor, Computer Science
Professor Viola’s expertise lies in scientific visualization and visual computing. His research focuses on multiscale visualization and modeling of complex biological systems, ranging from cellular organisms to atomistic-level details.
<> cloud computing (9)

Exploring FPGAs for Virtualized In-Network Acceleration

Dr. Suhaib Fahmy, Associate Professor, Computer Engineering, University of Warwick, UK

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B2 B3 A0215

Abstract With increasing connectivity and reliance on machine intelligence to process ever-growing amounts of data, the question of how to arrange the required connectivity and computation arises. Traditional cloud computing approaches that centralize compute capability in a data center do not scale well to large scale distributed data sources that must then transmit data over constrained networks. Similarly, computing at the very edge of the network is often constrained by limited computational capacity and a lack of access to shared data. In-network computing has been proposed as a way of

Computing in the Specialization Era

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B9 L2 H2 H2

In this talk I will discuss the shift towards hardware acceleration and show with several examples from industry and from research the large role that FPGAs are playing. I will hypothesize that we are in a new era where most of the established assumptions, rules of thumb, and accumulated wisdom about many aspects of computation in general and of data processing in particular no longer hold and need to be revisited.

Facebook AI new language explained by KAUST Professor Xiangliang Zhang

1 min read ·
That is not a Messenger troll, but the actual conversation between two chatbots by the Facebook AI Research (FAIR). The two robots, Bob and Alice, were thought the art of negotiation apples and books. They had been instructed to work out how to negotiate between themselves and improve their bartering as they went along. But, after leaving the pair alone, they start talking in this uncompressible but yet effective vocabulary.

Zuhair Yarub Khayyat

Zuhair Yarub Khayyat obtained his Ph.D. degree in Computer Science under the supervision of Professor Panagiotis Kalnis at InfoCloud Research Group (CLOUD) in King Abdullah University of Science and Technology (KAUST) in 2016. Research Interest Zuhair's research interests included Big Data Management, Parallel and distributed data processing, Cloud Computing and Graph Analytics. His main experience was focused on problem-solving and algorithm development for processing large data in distributed environments. He is a reliable team player with a methodological mindset with the ability to utilize

Marco Canini

Associate Professor, Computer Science
Professor Canini’s research seeks to improve networked-system design, implementation and operation concerning vital properties such as reliability, performance, security and energy efficiency.

Karim Awara

Karim Awara obtained his Master's Degree in Computer Science under the supervision of Professor Panagiotis Kalnis from King Abdullah University of Science and Technology. Research Interests Karim's research interests included the Database and High-Performance Computing field. Awards and Distinctions Awarded, internationally, as one of the best 5 teams in SIGMOD 2010 contest for developing high multi-query optimization techniques. And presented the work at SIGMOD conference, June 2010 in Indianapolis, IN, USA. Rewarded as one of the top four teams in Egypt in a software Engineering competition

Faisal Nawab

Faisal Nawab graduated with an MS degree in Computer Science from King Abdullah University of Science and Technology (KAUST) in 2011. Nawab currently works as an Assistant Professor of computer science and engineering in the Department of Computer Science and Engineering at the University of California, Santa Cruz, where he works on developing‏ systems for quick and accurate data analysis of new applications in cloud computing and Big Data. Education and Early Career Nawab earned a B.S degree in Computer Science from the KFUPM (King Fahd University of Petroleum and Minerals). He then studied

Essam Mansour

Postdoctoral Research Fellow, ‌InfoCloud
Essam Mansour was a Postdoctoral Research Fellow at InfoCloud Research Group (CLOUD) at King Abdullah University of Science and Technology (KAUST), worked with Professor Panagiotis Kalnis. He contributed to research activities of the KAUST InfoCloud Research Group including leading a research project aiming at developing an elastic database system for processing very large strings based on the cloud infrastructure. This system is to support scientific data management, as a data-intensive application. Prior to joining KAUST, Essam was a Research Associate at the School of Information Technology

Asaad S. Alghamdi

<> computational imaging (9)

Space-Time Tomographic Reconstruction of Deforming Objects

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B1 L2 R2202

In this thesis, a variety of applications in computer vision and graphics of inverse problems using tomographic imaging modalities will be presented: (i) The first application focuses on the CT reconstruction with a specific emphasis on recovering thin 1D and 2D manifolds embedded in 3D volumes. (ii) The second application is about space-time tomography (iii) Base on the second application, the third one is aiming to improve the tomographic reconstruction of time-varying geometries undergoing faster, non-periodic deformations, by a warp-and-project strategy. Finally, with a physically plausible divergence-free prior for motion estimation, as well as a novel view synthesis technique, we present applications to dynamic fluid imaging which further demonstrates the flexibility of our optimization frameworks

Optimization and Learning in Computational Imaging

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B9 H1 R2322

The Machine Learning Hub Seminar Series presents “Optimization and Learning in Computational Imaging” by Dr. Wolfgang Heidrich, Professor in Computer Science at KAUST. He leads the AI Initiative and is the Director of the KAUST Visual Computing Center. Computational imaging systems are based on the joint design of optics and associated image reconstruction algorithms. Historically, many such systems have employed simple transform-based reconstruction methods. Modern optimization methods and priors can drastically improve the reconstruction quality in computational imaging systems. Furthermore, learning-based methods can be used to design the optics along with the reconstruction method, yielding truly end-to-end learned imaging systems, blurring the boundary between imaging hardware and software.

Jinhui Xiong wins Best Paper Award at VMV 2019

1 min read ·
Fourth-year computer science Ph.D. student Jinhui Xiong won the best paper award at this year’s 24th International Symposium on Vision, Modeling, and Visualization (VMV) 2019.

Yidan Zheng

Ph.D. Student, Computer Science
Yidan is a MS/PhD student in Computer Science at King Abdullah University of Science and Technology since August 2022, under the supervision of Prof. Wolfgang Heidrich in Computational Imaging Group. Education and Early Career Yidan obtained her bachelor’s degree in Measurement and Control Technology and Instrument from University of Science and Technology of China in 2020. Research Interest Yidan is interested in developing novel computational imaging systems in both hardware and software.

Xinge Yang

Ph.D. Student, Computer Science
Xinge Yang is a computer science PhD student at KAUST, working on computational optics and computational imaging.

Qiang Fu

Research Scientist, Computer Science
Qiang Fu is a Research Scientist in the Computational Imaging Group (VCCIMAGING) working with Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Qiang Fu obtained his bachelor degree in Manufacturing and Design Automation of Machinery from University of Science and Technology of China in 2007. He has a Ph. D. in Optical Engineering from University of Chinese Academy of Sciences (2012). In 2012, Qiang joined the Academy of Opto-Electronics at Chinese Academy of Sciences as an Assistant Research Fellow. After 2 years

Julio Rey Ramirez

Ph.D. Student, Computer Science
Julio Rey Ramirez is a Master student under the supervision of Prof. Wolfgang Heidrich at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career Julio Rey Ramirez was an electronics engineer from National University of Colombia (UNAL). He worked as laboratory assistant in the lighting testing lab (LABE) at UNAL from 2015 to 2018. Julio Rey Ramirez joined KAUST as a visiting student in 2019, and worked in the programming of machine vision cameras and computational microscopy under the supervision of professor Wolfgang Heidrich

Jinhui Xiong

Ph.D. Student, Computer Science
Jinhui Xiong is a Ph. D. candidate in the Computational Imaging Group (VCCIMAGING) under the supervision of Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Jinhui Xiong obtained his bachelor degree in Electronic Engineering from City University of Hong Kong (CityU) in Hong Kong in 2015. Then, he joined KAUST and received his masters in Computer Science in 2017. During his bachelor study, he spent two years on CUDA programming for various projects in Dr. Jun Fan’s lab. In 2014, he worked as an assistant researcher at EMC

Guangming Zang

Research Engineer, Computer Science
Guangming Zang is a research engineer in the Computational Imaging Group (VCCIMAGING) under the supervision of Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Guangming obtained his bachelor degree in Software Engineering from China University of Geosciences in Wuhan, China in 2013. He received his master degree in Computer Science from University of Chinese Academy of Sciences in China. Before joining KAUST, Guangming was a research assistant (2013 – 2014) at The National Space Science Center at Chinese Academy of Sciences
<> data science (9)

On Knowledge Graph and Causality for Recommender Systems

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B1 L4 R4214

Knowledge Graph (KG) is a large-scale semantic network consisting of entities/concepts as well as the semantic relationships among them, which could be considered as a concise version of Semantic Web. Recently KG is emerging as a hot topic of knowledge discovery and management under artificial intelligence, facilitating semantic computing. Causal relation is a reflection of user behaviours with backend intention, which is related another emerging hot topic – recommendation interpretability. This talk will cover the recent research progresses in these two areas and highlight some open research challenges in recommender systems.

Digital Health: promises and pitfalls

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B9 L3 R3223

This talk will try to clarify some misconceptions about what digital health (DH) is, and what it should not be.

Designing High-Performance and Scalable Middleware for HPC, AI, and Data Science

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B9 L2 R2322 H1

This talk will focus on challenges and opportunities in designing middleware for HPC, AI (Deep/Machine Learning), and Data Science. We will start with the challenges in designing runtime environments for MPI+X programming models by considering support for multi-core systems, high-performance networks (InfiniBand and RoCE), GPUs, and emerging BlueField-2 DPUs. Features and sample performance numbers of using the MVAPICH2 libraries will be presented. For the Deep/Machine Learning domain, we will focus on MPI-driven solutions to extract performance and scalability for popular Deep Learning frameworks (TensorFlow and PyTorch), large out-of-core models, and Bluefield-2 DPUs.

Scalable Spatial Data Science for Social Scientists

Prof. Amr Magdy

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B4 L5 R5220

The proliferation of spatial data from modern and increasingly prevalent technologies has resulted in large datasets, ripe for extracting insightful knowledge that can drive many applications.

Lifting up female scientists

1 min read ·
Women in Data Science (WiDS) is like the Global Women's March for analytics; it's a phenomenon too big for just one city. WiDS is the largest data science conference on Earth—taking place in over 50 countries, it has attained more than 100,000 attendees and is tagged as a global movement. The annual conference aims to inspire and educate data scientists worldwide—regardless of gender—and support women in the field. This year's WiDS conference was held at Stanford University with more than 100 regional institutions all over the world participating, including KAUST.

Alumni Feature: Majed Alzahrani

1 min read ·
Working in one of the largest companies in the world is a dream come true for many. For Majed Alzahrani, a former Ph.D. student who worked under the supervision of Associate Professor Xin Gao, things couldn't be better. After graduating in 2017, he quickly joined Saudi Aramco as a data scientist in its Drilling Data Science unit.

Joining forces to advance healthcare analytics in the Kingdom

1 min read ·
The recent KAUST-Prince Mohammed Bin Salman College (MBSC) Invitational Healthcare Analytics and Data Science Workshop brought together top-level clinicians, healthcare executives, representatives to discuss digitization and advancement of healthcare analytics in Saudi Arabia.

Merey Ramazanova

Ph.D. Student, Computer Science
Merey Ramazanova is a Computer Science Ph.D. Student in Image and Video Understanding Lab (IVUL) Group at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Bernard Ghanem. Education and Early Career Merey obtained her bachelor's degree in Computer Science from Nazarbayev University in Kazakhstan in 2018. She also was a Research Intern at Okinawa Institute of Science and Technology (OIST) in Okinawa, Japan in 2018. Research Interest She is focusing on her research on data science, computer vision, and machine learning. Education Profile M.S., Computer

Abdulellah Abualshour

Abdulellah Abualshour is a Ph.D. student under the supervision of Prof. Bernard Ghanem at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career Abdulellah was chosen to join the KAUST Gifted Student Program (KGSP). He is a graduate of Rutgers University where he received a B.S. degree in Computer Science, with a focus on data science and artificial intelligence. Regarding his early career, Abdulellah did many internships during his studies. In 2015, he worked for 1 year as a Public Relations Officer at Rutgers U niversity in
<> text mining (9)

AI4GH Seminar Series - Variant Prioritization in Cancer: Understanding and Prediction of Cancer Driver Genes and Mutations

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B2 R5220

Sequencing has identified millions of somatic mutations in human cancers. Identifying and distinguishing cancer driver genes amongst the millions of candidate mutations remains a major challenge.

Variant Prioritization in Cancer: Understanding and Prediction of Cancer Driver Genes and Mutations

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B3 R5209

Abstract Sequencing has identified millions of somatic mutations in human cancers. Identifying and distinguishing cancer driver genes amongst the millions of candidate mutations remains a major challenge. Accurate identification of driver genes and driver mutations is critical for advancing cancer research and personalizing treatment based on accurate stratification of patients. Due to inter-tumor genetic heterogeneity, many driver mutations within a gene occur at low frequencies, which make it challenging to distinguish them from other non-driver mutations. Motivated by these challenges, we

Large-scale Text and Data Integration for Cancer Genomics Research

Prof. D, Rebholz-Schuhmann (University Cologne, Germany​)​

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B19 L3 H2

Abstract This talk will give an overview of data and text mining in the life science domain, but will also point out the use of semantic technologies for other domains. Such solutions cover the integration of heterogeneous data sources based on Semantic Web standards (e.g., TCGA, Cosmic, others), the development of smart technologies (e.g., stream processing), graph and literature analytics, and open data initiatives. Application domains are biomedical solutions in cancer genomics and targeted projects together with industrial partners exploiting Semantic Web technologies in general. The

Arwa Bin Raies defends her thesis proposal

1 min read ·
Arwa Bin Raies, a Ph.D. candidate under the supervision of Professor Vladimir Bajic, defended her thesis proposal on May 14, 2018.

Sara Althubaiti defends her Master's thesis

1 min read ·
Sara Althubaiti, a student under the supervision of Assistant Professor Robert Hoehndorf, defended her Master's thesis on November 5, 2018.

Sara Althubaiti

Ph.D. Student, Computer Science
Sara Althubaiti is currently a Ph.D. candidate in the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Robert Hoehndorf. Prior to this, Sara received her master's degree in computer science with a focus on bioinformatics from KAUST in December 2018. Research Interests Sara's research interests include bioinformatics, text mining, ontologies, and cancer. Her research focuses on applying machine learning methods in cancer biology and development specifically in the field of finding driver genes and mutations in

Robert Werfelmann

Robert Werfelmann obtained a Master's Degree in Computer Science program at Machine Intelligence and kNowledge Engineering (MINE) Laboratory under the supervision of Professor Xiangliang Zhang at King Abdullah University of Science and Technology (KAUST). He did his thesis in a task of Natural Language Processing where the goal is to predict the native language of the author of a non-native text. During his studies, he has built and improved skills in several topics including algorithm design and analysis, concurrent computing, computer networking, and artificial intelligence fields such as

Arwa Bin Raies

Research Interests I am a computer scientist whose primary focus is on developing novel machine learning methods to extract useful information from complex datasets. My experience, however, has covered intellectually wide-ranging areas spanning several disciplines in computer science, biology, and chemistry. At the undergraduate level, I focused on computer networks, and I did an internship for six months at Cisco Systems Inc., and I achieved CCNA and CCNP certification. At the Master's level, however, I joined the Computational Bioscience Research Center. I was in charge of a text-mining

Adil Salhi

Adil Salhi is a Research Engineer at Prof. Xin Gao's Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Adil obtained his Ph.D. degree in Computer Science from the University of Manchester, UK. Research Interests Adil's research interests lie in the area of data and text mining of biomedical literature. The extraction of new information from disparate structured data sources, or available information buried in free-form text, requires the deployment of a wide array of tools to overcome the challenges posed by the nature and size
<> algorithm (8)

Amin Allam (CEMSE PhD) wins the second prize in the DREAM Challenge 1B

1 min read ·
Amin Allam (CEMSE PhD student in the InfoCloud group) supervised by Prof Panos Kalnis has won the second prize in the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge 1B. Amin Allam utilized Bayesian inference to achieve the 2nd place.

New Data Mining approach makes recurring patterns easier to be spotted

1 min read ·
The ultimate answer is ScaleMine, cost-effective and agile scalable parallel frequent subgraph mining in a single large graph. Panagiotis Kalnis, Professor of Computer Science (CS), and the team from the KAUST Extreme Computing Research Center (ECRC), under the Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division signed this novel approach.

Algorithm scours datasets to diagnose medical mysteries

1 min read ·
A new tool uses genetic and clinical information to find the root cause of unexplained illnesses.

Dominik L. Michels receives the first Procter & Gamble Faculty Award

1 min read ·
Dominik L. Michels, Computer Science and Mathematics faculty in the Computer, Electrical and Mathematical Science and Engineering Division (CEMSE) and head of the Computational Sciences Group within the Visual Computing Center (VCC), recently received the first Procter & Gamble (P&G) Faculty Award.

Querying big data just got universal

1 min read ·
A universal query engine for big data that works across computing platforms could accelerate analytics research.

An algorithm for success: KAUST-designed differential equation-solving algorithm included in Maple 2020

1 min read ·
A KAUST-designed symbolic algorithm to solve nonlinear ordinary differential equations (ODEs) has been selected for inclusion in the current version of the computer algebra system (CAS) Maple 2020. The global symbolic and numeric computing software system—developed by the Canadian company Waterloo Maple (Maplesoft)—is widely used by scientists, engineers, and researchers to analyze, visualize, and solve mathematical problems.

Training an AI eye on the Moon

1 min read ·
Machine learning accelerates the search for promising Moon sites for energy and mineral resources.

Zhize Li

Research Scientist, Computer Science
Zhize Li is a Research Scientist working with Professor Peter Richtarik in his research group at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Zhize Li received his Ph.D. in Computer Science from the Institute for Interdisciplinary Information Sciences of Tsinghua University in 2019 under the supervision of Prof. Jian Li. On the career side, before joining KAUST, he was a visiting scholar at the Computer Science Department of Duke University (Hosted by Prof. Rong Ge) and a visiting scholar at the Industrial and
<> computational methods (8)

Peter Schmid

​Professor Schmid's research interests are in theoretical and computational fluid dynamics, with emphasis on hydrodynamic stability theory, flow control, model reduction and system identification. He is also interested in computational techniques for flow optimization and quantitative flow analysis.

Student life: studying and working at the Computational Bioscience Research Center (KAUST)

1 min read ·
For Ph.D. students Arturo Magana Mora, Haitham Ashoor and Olaa Motwalli, April 2017 was a particularly nerve-wracking month. After years of hard work and sleepless nights, they finally defended their thesis, bringing them one step closer to graduation.

The awesome scope of big data

1 min read ·
Sifting through huge amounts of data may bring a better understanding of whale shark social structures, protein targets for drug therapies and disease-causing genes.

Sarah Alghamdi defends her master's thesis

1 min read ·
Sarah Alghamdi successfully defended her master's thesis on April 10, 2018. Her project, entitled "Ontology Design Patterns for Combining Pathology and Anatomy: Application to Study Ageing and Longevity in Inbred Mouse Strains", was well-received by a panel of professors that included Professor Vladimir Bajic, Associate Professor Xin Gao and Assistant Professor Robert Hoehndorf.

Arwa Bin Raies defends her thesis proposal

1 min read ·
Arwa Bin Raies, a Ph.D. candidate under the supervision of Professor Vladimir Bajic, defended her thesis proposal on May 14, 2018.

Rawan Olayan

Rawan Olayan obtained both, her MS Degree and PhD Degree from Computer Science at KAUST in 2013 and 2018 respectively. Her master thesis and PhD thesis supervisor was Vladimir Bajic. Under the supervision of her advisor Professor Vladimir Bajic, she completed her thesis on developing novel computational methods to predict drug-target interactions and their functional effects using data integration, graph mining and machine learning approaches. Currently, Rawan is working as a postdoctoral associate in the Carter Lab at the Jackson Laboratory for Genomic Medicine in Farmington, Connecticut. In

Olaa A. Motwalli

Research Interests Olaa Motwalli is a Ph.D. student in Computational Science and a member of the Computational Bioscience Research Center (CBRC) at KAUST. She obtained her Bachelor's degree in Computer Science at King Abdul Aziz University in Jeddah and obtained her Master's degree in Computer and Software Engineering at Widener University in the USA. Her research interests are mainly the development and computational support for the design of sustainable biofuel production via microbial cell factories through machine learning, graph mining, and algorithm development, genome-scale metabolic

Haitham M. Ashoor

Research Interests Haitham Ashoor is a Ph.D. candidate in the Computer Science program at King Abdullah University of Science and Technology (KAUST). He is working under the supervision of Prof. Vladimir Bajic. He has obtained his BSc degree in Computer Engineering from the University of Jordan, Amman Jordan in 2008. He joined KAUST as an MSc student in 2009. In 2011, He finished his master studies under the supervision of Prof. Vladimir Bajic. His main research interests are developing computational methods for next-generation sequencing data analysis and applications of machine learning in
<> healthcare (8)

Babar Khan

Ph.D. (former), Bioscience
Ph.D. degree in King Abdullah University of Science and Technology (KAUST)

Ahsan Mehmood

Intern (former), Information Science Lab
Visiting student at King Abdullah University of Science and Technology (KAUST).

Deep Predictive Models for Mining Electronic Health Records

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B1 L4 R4214

There is an increasing growth in the amount of electronic health records (EHRs) being collected by healthcare facilities. Data mining techniques hold great potential to systematically use such data for identifying not only inefficiencies but also best practices that improve care and reduce costs. However, due to the complexity of EHR data, directly applying traditional machine learning techniques may yield unsatisfactory predictive performance. Recent advances in deep learning-based methods provide unprecedented ability to predict patients’ future health status, but they still suffer from the sparsity issue of EHR data.

Talented KAUST Ph.D. student aims to revolutionize healthcare with machine learning

1 min read ·
KAUST Ph.D. student, Yu Li, is a talented young computer scientist with an interest in developing novel computational methods and algorithms to solve and understand the principles behind the “bio-world.”

First joint publication between KAUST and MBSC seeks to improve treatment options for chronic kidney disease in Saudi Arabia

1 min read ·
Chronic kidney disease (CKD) is among the most common causes of death in Saudi Arabia. According to the 2018 edition of the Institute for Health Metrics and Evaluation’s annual Financing Global Health report, the prevalence of CKD among the Kingdom’s general population stands at 5.44% and accounts for a 4.04% mortality rate.

Safeguarding the right to be forgotten

1 min read ·
An open-source software can help align artificial intelligence applications in healthcare with data privacy regulations.

Sarah Alghamdi

Ph.D. Student, Computer Science
Sarah Alghamdi is a Ph.D. candidate in the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Robert Hoehndorf. Research Interests Sarah's research interests include applications of artificial intelligence and using statistical methods to genomics and healthcare. Professional Profile Program Committee for the International Conference of Biomedical Ontology (ICBO 2022) Program Committee for the International Society of Molecular Biology (ISMB 2022) - Bio-Ontology track 2018-2021: Teaching Assistant positions: CS

Adeeb Noor

Visiting Professor, Bio-Ontology Research Group
Adeeb Noor is an Associate Professor of Computer Science at King Abdulaziz University (KAU), where he is the founder of the King Abdulaziz University Hospital patients portal (SHIFAA). He is also a visiting professor and part of the Bio-Ontology Research Group in KAUST. Research Interests Dr. Noor areas of research include but are not limited to data and business governance, healthcare digital transformation, drug-drug interactions, and personalized medicine. Selected Publications​ Agent-enabled task offloading in UAV-aided mobile edge computing, R Wang, Y Cao, A Noor, TA Alamoudi, R Nour -
<> Natural Language Processing (8)

Leveraging Readily Available Data for Low Resource Natural Language Processing

Derry Wijaya, Assistant Professor, Computer Science, Boston University

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KAUST

State-of-the-art Natural Language Processing (NLP) systems nowadays are dominated by machine learning and deep learning models. However, most of these models often only work well when there are abundant labeled data for training. Furthermore, as these models typically have a large number of parameters, they require large compute resources to train. For the majority of languages in the world and the researchers working on these languages, however, abundant labeled data are a privilege and so do compute resources. How can we train generalizable NLP models that are effective even when labeled data are scarce and compute resources are limited? In this talk, I will present some of our solutions that leverage unsupervised or few-shot learning and readily available multilingual resources or multimodal data to improve machine translation and nuanced text classification such as news framing under these low resource settings.

Rising Stars in AI Symposium 2022 at KAUST

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B4 B5 A0215

Rising Stars in AI Symposium 2022 at KAUST The "Rising Stars in AI Symposium" is geared towards young researchers (including PhD students, PostDocs, young faculty) who have recently published promising work at leading venues. There will be dozens of brief in-person presentations about papers recently accepted at major AI conferences such as NeurIPS, CVPR, EMNLP, ACL, ICML, ICLR, etc. All speakers will be encouraged to start with an intro for non-AI experts. To view the complete program and for more event details, please visit the symposium website. Attend the Symposium The symposium will be

Navigating NLP for Underrepresented Languages: Dataset Challenges, Efficient Techniques, and Evaluations

Fajri Koto, Postdoc, MBZUAI

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B9 L4 R4225

Democratizing NLP across numerous languages is a non-trivial task, as it may encounter challenges related to data scarcity, limitations in computational resources, and the intricacies of multilingual and multicultural diversity. The speaker will discuss the efforts and findings in tackling these challenges in this talk. To begin, data scarcity and inconsistency in metadata present common obstacles in low-resource NLP, complicating the understanding of the NLP landscape for low-resource languages.

KAUST Prospective student: Fernando Patricio Zhapa Camacho

1 min read ·
Fernando is a 23-year-old Information Technology graduate fromYachay Tech University in Urcuquí, Ecuador. He has planned to continue his academic career at KAUST under the supervision of Professor Robert Hoehndorf.

Meet KAUST Prospective Student: Asaad AlGhamdi

1 min read ·
Asaad AlGhamdi is a 24-year-old graduate who will join KAUST in the fall from King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia. Asaad will join the University as a M.S. candidate and member of the Machine Intelligence and kNowledge Engineering research group under the supervision of Professor Xiangliang Zhang.

Meet KAUST new student: Xiuying Chen

1 min read ·
Xiuying Chen is a computer science graduate who joined KAUST from Wuhan University, China. Chen believes that KAUST provides an excellent research environment to carry out scientific research.

Xiuying Chen

Ph.D. Student, Computer Science
Xiuying Chen received her B.S. degree in Computer Science from Wuhan University in 2018, followed by a Master's degree from Wangxuan Institute of Computer Technology at Peking University. She is currently a Ph.D. student at King Abdullah University of Science and Technology (KAUST), focusing on improving accuracy, robustness, and trustworthiness of Text Generation. She is also interested in interdisciplinary research, such as in applying large language models within healthcare and scientific discovery. She has made significant contributions with over 40 papers published in top-tier conferences

Robert Werfelmann

Robert Werfelmann obtained a Master's Degree in Computer Science program at Machine Intelligence and kNowledge Engineering (MINE) Laboratory under the supervision of Professor Xiangliang Zhang at King Abdullah University of Science and Technology (KAUST). He did his thesis in a task of Natural Language Processing where the goal is to predict the native language of the author of a non-native text. During his studies, he has built and improved skills in several topics including algorithm design and analysis, concurrent computing, computer networking, and artificial intelligence fields such as
<> reconfigurable computing (8)

Ussama Zahid

Ph.D. Student, Computer Science
Ussama Zahid is a PhD student in the Accelerated Connected Computing Lab at KAUST. He works at the intersection of deep learning and embedded systems, i.e., “Edge AI”. He specializes in efficient training and inference of quantized deep neural networks (DNNs) and hardware-software co-design of DNN accelerators. Before joining KAUST, he was as a research engineer intern at Xilinx Research Labs, Dublin Ireland, Ireland where he worked on quantized and robust training of Deep Neural Networks (DNNs), resulting in patents and publications. He received his Bachelor's degree in Electrical Engineering

Suhaib Fahmy

Associate Professor, Computer Science
Professor Fahmy’s research explores novel ways to integrate accelerated computation and communication to improve computing systems' efficiency, latency, and security.

Rayan Rozi

Ph.D. Student, Computer Science
Rayan Rozi is a PhD student in the Accelerated Connected Computing Lab at KAUST. He works on the design of hardware accelerators. He received his Bachelor's degree in Computer Science from King Abdulaziz University, Saudi Arabia in 2020. He received his Masters in Computer Science from KAUST in 2022.

Mohamed Hassan

Postdoctoral Research Fellow, Computer Science
Mohamed W. Hassan is a Postdoctoral Research Fellow in the Accelerated Connected Computing Lab at KAUST. He works on reconfigurable computing and computer architecture for high performance computing. He received his PhD in Electrical and Computer Engineering from Virginia Tech in 2021. He received Bachelor's and Master's degrees in Computer Engineering from the Arab Academy of Science, Egypt in 2010 and 2013 respectively. Mohamed's research has explored accelerating scientific applications in heterogenous systems including CPUs, GPUs, and FPGAs. He is especially interested in optimizing High

Mohamed Bouaziz

Ph.D. Student, Computer Science
Mohamed Bouaziz is a PhD student in the Accelerated Connected Computing Lab at KAUST working on electronic design automation for reconfigurable computing.

Mahmoud Elshenawy

Ph.D. Student, Computer Science
Mahmoud Elshenawy is an MS/PhD student in the Accelerated Connected Computing Lab at KAUST. He works on reconfigurable computing. He received his Bachelor of Science in Computer Engineering from the American University of Cairo, Egypt in 2023.

Krishnan Iyer

Ph.D. Student, Computer Science
Krishnan is an MS/PhD student in the Accelerated Connected Computing Lab at KAUST. He works on reconfigurable computing systems. He received his BTech degree in Electrical Engineering from Amrita Vishwa Vidyapeetham University, India in 2020 and worked as an Embedded Systems Engineer at Peer Robotics.

Hao Liu

Ph.D. Student, Computer Science
Hao Liu is a PhD candidate in the Accelerated Connected Computing Lab at KAUST. He works on reconfigurable computing systems, and has explored machine learning model compression and physically unclonable functions. He received his Bachelor's degree in Electronic and Information Engineering and his Master's degree in Cybersecurity and Technology from Beihang University, Beijing, China in 2019 and 2021, respectively.
<> biology (7)

AI4GH Seminar Series | Predicting Protein Function and Phenotype Associations

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B2 R5220

The amount of available protein sequences is rapidly increasing, mainly as a consequence of the development and application of high ​throughput sequencing technologies in the life sciences.

Genetic Algorithms for Optimization of Machine-learning Models and their Applications in Bioinformatics

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B3 L5 R5209

Abstract ​​​Machine-learning (ML) techniques have been widely applied to solve different problems in biology. However, biological data are large and complex, which often results in extremely intricate ML models. Frequently, these models may have poor performance or may be computationally unfeasible. This study presents a set of novel computational methods and focuses on the application of genetic algorithms (GAs) for the simplification and optimization of ML models and their applications to biological problems. The dissertation addresses the following three challenges. The first challenge is

AI4GH Seminar Series| Artificial Intelligence for Genomics and Health: Introduction and Overview

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B2 R5220

Abstract Genomics and health are prime areas for the development and application of novel intelligent algorithms due to a large amount of available research data, the high complexity of the problem, and the significant social and economic benefits of improving health outcomes in humans. In the past, genomics and health have been one of the key drivers in the development of Artificial Intelligence methods, including machine learning, expert systems, or graph-based algorithms. I will introduce the new seminar series on Artificial Intelligence for Genomics and Health (AI4GH), and then discuss our

Peak performance for proteins

1 min read ·
An improved peak fitting procedure enables a better determination of protein structures.

Disease researchers have a way with words

1 min read ·
Specialized word search method sheds light on phenotypic similarity of diseases.

The awesome scope of big data

1 min read ·
Sifting through huge amounts of data may bring a better understanding of whale shark social structures, protein targets for drug therapies and disease-causing genes.

Ivan Viola

Professor, Computer Science
Professor Viola’s expertise lies in scientific visualization and visual computing. His research focuses on multiscale visualization and modeling of complex biological systems, ranging from cellular organisms to atomistic-level details.
<> biomedicine (7)

AI4GH Seminar Series | Predicting Protein Function and Phenotype Associations

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B2 R5220

The amount of available protein sequences is rapidly increasing, mainly as a consequence of the development and application of high ​throughput sequencing technologies in the life sciences.

Ontology Design Patterns for Combining Pathology and Anatomy: Application to Study Ageing and Longevity in Inbred Mouse Strains

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B9 R3120

Abstract In biomedical research, ontologies are widely used to represent knowledge as well as annotate datasets. Many of the existing ontologies cover a single type of phenomena, such as a process, cell type, gene, pathological entity or anatomical structure. Consequently, it is required to use multiple ontologies to fully characterize the observations in the datasets. Although this allows precise annotation of different aspects of a given dataset, it limits our ability to use the ontologies in data analysis, as the ontologies are usually disconnected and their combination cannot be exploited

AI4GH Seminar Series| Artificial Intelligence for Genomics and Health: Introduction and Overview

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B2 R5220

Abstract Genomics and health are prime areas for the development and application of novel intelligent algorithms due to a large amount of available research data, the high complexity of the problem, and the significant social and economic benefits of improving health outcomes in humans. In the past, genomics and health have been one of the key drivers in the development of Artificial Intelligence methods, including machine learning, expert systems, or graph-based algorithms. I will introduce the new seminar series on Artificial Intelligence for Genomics and Health (AI4GH), and then discuss our

Causality-based new drug development: Some successful cases and a new challenge

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B2 L5 R5209

Abstract The success rate of new drug development is extremely low. It is even lower when the new drug has a novel mechanism of action. From my experience, I propose that the success rate can be dramatically increased by predicting the effects of a drug in humans based on the causality-confirmed data. It is dangerous to develop new drugs based on the data in which causality is not confirmed. In biology, there are three different types of relationships in which the causality is confirmed, i.e. the relationships between parent and child, between gene and phenotype and between intervention and

Accelerating biomedical discovery with an Internet of FAIR data and services

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B9 L2 H1 R2322

In this talk, I will discuss our work to create computational standards, platforms, and methods to wrangle knowledge into simple, but effective representations based on semantic web technologies that are maximally FAIR - Findable, Accessible, Interoperable, and Reuseable - and to further use these for biomedical knowledge discovery. But only with additional crucial developments will this emerging Internet of FAIR data and services enable automated scientific discovery on a global scale.

Towards Accurate Biomedical Genomics Anywhere Anytime - Graduate Seminar

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B9 L2 H1 R2322

Abstract Current genetic diagnosis by next-generation sequencing requires a large investment of resources and offers little point-of-care portability. Furthermore, it is unable to detect many types of genetic variations - including large deletions, duplications, and balanced translocations - that are relevant to human diseases and health. Comparing to other sequencing technologies, Nanopore sequencing owns the advantages of point-of-care (i.e., sequencing anywhere anytime), long reads (i.e., assembly-free to detect various genetic variations), and PCR free (i.e., sample preparation is easy)

Disease researchers have a way with words

1 min read ·
Specialized word search method sheds light on phenotypic similarity of diseases.
<> cancer (7)

AI4GH Seminar Series - Variant Prioritization in Cancer: Understanding and Prediction of Cancer Driver Genes and Mutations

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B2 R5220

Sequencing has identified millions of somatic mutations in human cancers. Identifying and distinguishing cancer driver genes amongst the millions of candidate mutations remains a major challenge.

Variant Prioritization in Cancer: Understanding and Prediction of Cancer Driver Genes and Mutations

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B3 R5209

Abstract Sequencing has identified millions of somatic mutations in human cancers. Identifying and distinguishing cancer driver genes amongst the millions of candidate mutations remains a major challenge. Accurate identification of driver genes and driver mutations is critical for advancing cancer research and personalizing treatment based on accurate stratification of patients. Due to inter-tumor genetic heterogeneity, many driver mutations within a gene occur at low frequencies, which make it challenging to distinguish them from other non-driver mutations. Motivated by these challenges, we

AI4GH Seminar Series - Genome-scale Regression Analysis Reveals a Linear Relationship for Promoters and Enhancers After Combinatorial Drug Treatment

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B2 R5220

Drug combination therapy for the treatment of cancers and other multifactorial diseases has the potential of increasing the therapeutic effect while reducing the likelihood of drug resistance. In order to reduce the time and cost spent on comprehensive screens, methods are needed which can model additive effects of possible drug combinations.

Sara Althubaiti defends her Master's thesis

1 min read ·
Sara Althubaiti, a student under the supervision of Assistant Professor Robert Hoehndorf, defended her Master's thesis on November 5, 2018.

Algorithm turns cancer gene discovery on its head

1 min read ·
A method for finding genes that spur tumor growth takes advantage of machine learning algorithms to sift through reams of molecular data collected from studies of cancer cell lines, mouse models and human patients.

New database to "SpUR" on cancer research

1 min read ·
Xin Gao's team developed an interactive web portal where cancer scientists can interrogate how RNA splicing in noncoding parts of genes fuels the growth of different types of tumors.

Sara Althubaiti

Ph.D. Student, Computer Science
Sara Althubaiti is currently a Ph.D. candidate in the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Robert Hoehndorf. Prior to this, Sara received her master's degree in computer science with a focus on bioinformatics from KAUST in December 2018. Research Interests Sara's research interests include bioinformatics, text mining, ontologies, and cancer. Her research focuses on applying machine learning methods in cancer biology and development specifically in the field of finding driver genes and mutations in
<> communication networks (7)

Alaa Alameer Ahmad

Research visit to communication theory lab in computer, electrical and mathematical sciences and engineering. KAUST (King Abdullah University of Science and Technology) ,Worked with research groups supervised by Prof. Mohamed-Slim Alouini and Prof. Tareq AL-Naffouri.

Learning-Based Approaches for Next-Generation Intelligent Networks

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KAUST

In this thesis, we investigate how learning-based approaches are implemented to solve the communication network problems and how communication network dependencies impact the training of learning-based approaches.

Dr. Basem Shihada and Dr. Sajid Ahmed promoted to Senior Member of IEEE

1 min read ·
Dr. Basem Shihada and Dr. Sajid Ahmed have been elevated to the rank of Senior Member of IEEE. This is considered a significant honor since only 8% of nearly 400,000 IEEE members are Senior Members because of their superior professional achievements.

KAUST's Prof. Basem Shihada's work on Buffer Sizing for Multi-Hop Networks, granted US Patent 8,638,686

1 min read ·
Prof. Basem Shihada and his previous research scientist Kamran Jamshaid obtained a US Patent for their technology on optimizing buffer sizing for multi-hop networks.

A breath of fresh information to help diagnosis

1 min read ·
A communications concept could pinpoint a person infected with a deadly, contagious virus in the middle of a crowded airport.

Liang Zhang

Liang Zhang is a Ph.D. student in the Networking Research Lab (NETLAB), at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Basem Shihada. Liang received a master's degree in Electrical Engineering from King Abdullah University of Science and Technology (KAUST) and a bachelor's degree in Physics from the University of Science and Technology, Beijing (USTB), China. Thesis title: Machine learning for next generation IoT Systems. In progress since Sept. 2018. Research Interests Liang's research interests include IoT, UAV, reinforcement learning

Basem Shihada

Professor, Computer Science
Professor Shihada research philosophy is to bridge the gap between theory and practice in a working system. Having intensively worked in computer networks from both theoretical and systems aspects, Shihada has acquired both a solid mathematical background and an acute sense of applying these results to build practical systems.
<> communications (7)

Dr. Basem Shihada and Dr. Sajid Ahmed promoted to Senior Member of IEEE

1 min read ·
Dr. Basem Shihada and Dr. Sajid Ahmed have been elevated to the rank of Senior Member of IEEE. This is considered a significant honor since only 8% of nearly 400,000 IEEE members are Senior Members because of their superior professional achievements.

KAUST's Prof. Basem Shihada's work on Buffer Sizing for Multi-Hop Networks, granted US Patent 8,638,686

1 min read ·
Prof. Basem Shihada and his previous research scientist Kamran Jamshaid obtained a US Patent for their technology on optimizing buffer sizing for multi-hop networks.

Prof. Osama Amin Elevated To Senior Member Of IEEE

1 min read ·
Dr. Osama Amin has been elevated to the rank of Senior Member of IEEE. This is considered an important honor since only 8% of nearly 400,000 IEEE members are Senior Members because of their superior professional achievements.

No strings attached for underwater video system

1 min read ·
An underwater wireless optical communications system for streaming high-quality, live video.

A clearer future for underwater exploration

1 min read ·
An optical communication system could revolutionize underwater exploration and discovery.

Angling for underwater WiFi

1 min read ·
Scuba divers could send sea life shots in real time using an aquatic internet service.

Basem Shihada

Professor, Computer Science
Professor Shihada research philosophy is to bridge the gap between theory and practice in a working system. Having intensively worked in computer networks from both theoretical and systems aspects, Shihada has acquired both a solid mathematical background and an acute sense of applying these results to build practical systems.
<> data visualisation (7)

Keynote Speaker | The 8th BEAR PGR Conference & Users Forum 2018

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The University of Birmingham in the United Kingdom

Abstract KAUST Assistant Professor Robert Hoehndorf will be a keynote speaker at the 8th BEAR PGR Conference & Users Forum​ at the University of Birmingham in the United Kingdom. This event focuses on, but not limited to, computational analysis and numerical modeling the conference will cater to researchers of all schools interested in BEAR facilities. Such as the use of the high-performance computing (HPC) system, BlueBEAR, cloud storage or data visualization.​

Task-driven Automated Data Visualization

Dr. Yunhai Wang, Professor, Computer Science, Shandong University, China

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B9 L2 H2 R2325

Abstract By providing visual representations of data, visualization can help people to carry out many data analysis tasks more effectively. Given a data set, however, there are often too many possible visualization techniques, with each technique having many parameters to be tuned. This arises the question is if it is possible to automatically design a visualization that is best suited to pursue a given task on given input data. With my group, I developed a number of techniques to achieve this goal for different data sets such as time-series data, high-dimensional data, text as well as graphs

Large-Scale Visualization of Scientific Data

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KAUST

"A picture is worth a thousand words", and by going beyond static images, interactive visualization has become crucial to exploring, analyzing, and understanding large-scale scientific data. This is true for many areas of science and engineering, such as high-resolution imaging in neuroscience or materials science, as well as in large-scale fluid simulations of the Earth’s atmosphere and oceans, or of trillion-cell oil reservoirs. However, the fact that the amount of data in data-driven sciences is increasing rapidly toward the petascale, and further, presents a tremendous challenge to interactive visualization and analysis. Nowadays, an important enabler of interactivity is often the parallel processing power of GPUs, which, however, requires well-designed customized data structures and algorithms. Furthermore, scientific data sets do not only get larger, they also get more and more complex, and thus have become very hard to interpret and analyze. In this talk, I will give an overview of the research of my group in large-scale scientific visualization, from data structures and algorithms that enable petascale visualization on GPUs, to novel visual abstractions for interactive analysis of highly complex structures in neuroscience, to novel mathematical techniques that leverage differential geometric methods for the detection and visualization of features in large, complex fluid dynamics data on curved surfaces such as the Earth.

Thoughts on how to Effectively Explore Visual Representations in Mixed Reality

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B9 L2 R2322 H1

In this talk I will report on various research projects that I carried out with my students to better understand the interaction landscape and will report on lessons we learned. I will focus mostly on AR-based setups with application examples from physical flow visualization, molecular visualization, visualization of particle collisions, biomolecular dynamics in cells, and oceanography. I will show interaction techniques that rely on purely gestural interaction, phones or tablets as input and control devices, and hybrid setups that combine traditional workstations with AR views. I will discuss navigation, data selection, and visualization system control as different interaction tasks. With this overview I aim to provide an understanding of typical challenges in immersive visualization environments and how to address some of these challenges.

Dr. Ciril Bohak Joins the Visual Computing Center

1 min read ·
We are proudly announcing that Dr. Ciril Bohak has joined VCC as a Postdoctoral researcher of Computer Science in October, 2020. Ciril Bohak is a Postdoctoral Researcher and Teaching Assistant in the Laboratory for Computer Graphics and Multimedia, Faculty of Computer and Information Science, University of Ljubljana, Slovenia. His main research interests are Computer Graphics, Data Visualization, and Game Technology. His current research includes real-time medical and biological volumetric data visualization on the web, visualization of geodetic data (LiDAR and orthophoto) and high-energy

Juris Grigs

Ph.D. Student, Computer Science
Juris Grigs is a Computer Science Ph.D. student in the KAUST High-Performance Visualization Group (VCCVis) under the supervision of Professor Markus Hadwiger at King Abdullah University of Science and Technology (KAUST). Education and Early Career Juris obtained his bachelor's degree in ​Physics from the University of Latvia in 2017. He joined KAUST as an Electrical Engineering student with the Integrated Microwaves Packaging Antennas & Circuit Technology (IMPACT) Research Group. Now,he is a Ph.D. student in the VCCVis Research Group. Research Interests Juris's research interests include ​data

Ciril Bohak

Research Scientist, Computer Science
Ciril Bohak was a Research Scientist in the NANOVIS Group, working with Professor Ivan Viola at King Abdullah University of Science and Technology (KAUST). He was a member of the Nanovisualization Research Group and works on development of novel rendering and visualization methods for visualizing data at the nano scale. Education and Early Career Ciril obtained his B.Sc., M.Sc. and Ph.D. in Computer Science from University of Ljubljana. He started his career as researcher in Laboratory for Computer Graphics and Multimedia researching in fields Music Information Retrieval, Computer Graphics and
<> GPU Computing (7)

Wajih Halim Boukaram

Ph.D. Student, Computer Science
Wajih Halim Boukaram is a PhD candidate in Computer Science. He is studying under the supervision of Professor David E. Keyes.

Peter Rautek

Research Scientist, Computer Science
Peter Rautek is a Research Scientist in the High-Performance Visualization Group working with Professor Markus Hadwiger at King Abdullah University of Science and Technology (KAUST).

Nicolas Doucet

Nicolas Doucet is a PhD student in Computer Science, working under the supervision of Professor David Keyes.

Marcin Rogowski

Ph.D. Student, Computer Science

Jens Schneider

Research Scientist, Computer Science
Jens Schneider was a Staff Scientist at the Visual Computing Center in King Abdullah University of Science and Technology (KAUST). Currently, he’s an Assistant Professor in College of Science and Engineering, Hamad Bin Khalifa University (HBKU), Qatar. Education and Early Career Jens Schneider received a diploma equivalent to Master degree in Computer Science from RWTH Aachen in Germany, 2004. He earned his Ph.D. in Computer Science from Technical University Munich, Germany, in 2009 Dr. Schneider joined KAUST in 2009 as a Postdoctoral Research Fellow. Later on, he was promoted to Research

Amani Alonazi

Amani Alonazi is a Visiting Researcher in the Image and Viedo Understanding Lab (IVUL) Research Group working with Professor Bernard Ghanem. Previously, she was a Ph.D. candidate in Computer Science at CEMSE, KAUST, and a member of KAUST's Extreme Computing Research Center, ACM, and SIAM. Areas of expertise and current scientific interests Amani's research focuses on developing novel asynchronous task-based algorithms of solvers in scientific simulations. The core of her work lies within the domain of high-performance computing, which combines performance optimizations, modeling, and

Ali Charara

Ali Charara obtained both, his MS Degree and PhD Degree from Computer Science at KAUST in 2011 and 2018 respectively. His master thesis supervisor was Alyn Rockwood and his PhD thesis supervisor was David Keyes. Ali's research interests were in High Performance Computing with a focus on parallel computing and algorithms using multicore and manycore Computer Graphics Units. Education Profile MS Degree, Computer Science, KAUST (2011) PhD Degree, Computer Science, KAUST (2018)
<> graph mining (7)

Novel Computational Methods to Predict Drug–target Interactions Using Graph Mining and Machine Learning Approaches

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B3 L5 R5220

Abstract Computational drug repurposing aims at finding new medical uses for existing drugs. The identification of novel drug-target interactions (DTIs) can be a useful part of such a task. Finding computationally DTIs is a convenient strategy to identify potentially new DTIs at low cost with reasonable accuracy. However, the current DTI prediction methods suffer a high false positive prediction rate. Here, we present a comprehensive review of the recent progress in the field of DTI prediction from data-centric and algorithmic-centric perspectives that can help in constructing novel reliable

Novel Computational Methods that Facilitate Development of Cyanofactories for Free Fatty Acid Production by Olaa Motwalli

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B3 L5 R5209

Abstract Finding a source from which high-energy-density biofuels can be derived at an industrial scale has become an urgent challenge for renewable energy production. Some microorganisms can produce free fatty acids (FFA) as precursors towards such high-energy-density biofuels. In particular, photosynthetic cyanobacteria are capable of directly converting carbon dioxide into FFA. However, current engineered strains need several rounds of engineering to reach the level of FFA production for it to be commercially viable. Thus, new chassis strains that require less engineering are needed

Drug Repositioning through the Development of Diverse Computational Methods using Machine Learning, Deep Learning, and Graph Mining

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KAUST

In this dissertation, we combined artificial intelligence and machine/deep learning with chemical and biological properties to develop several computational methods to solve biomedical domain problems, specifically drug repositioning, and demonstrated their efficiencies and capabilities. We developed three network-based DTI prediction methods using machine learning, graph embedding, and graph mining. These methods significantly improved prediction performance, and the best-performing method even reduces the error rate by more than 33% across all datasets compared to the best state-of-the-art method. As it is more insightful to predict continuous values that indicate how tightly the drug binds to a specific target, we conducted a comparison study of current regression-based methods that predict drug-target binding affinities (DTBA). Our methods demonstrated their efficiency and capability by achieving high prediction performance and identifying therapeutic targets for several cancer types. We further conducted a lung cancer case study of findings that support the novel predicted targets.

Student life: studying and working at the Computational Bioscience Research Center (KAUST)

1 min read ·
For Ph.D. students Arturo Magana Mora, Haitham Ashoor and Olaa Motwalli, April 2017 was a particularly nerve-wracking month. After years of hard work and sleepless nights, they finally defended their thesis, bringing them one step closer to graduation.

Rawan Olayan

Rawan Olayan obtained both, her MS Degree and PhD Degree from Computer Science at KAUST in 2013 and 2018 respectively. Her master thesis and PhD thesis supervisor was Vladimir Bajic. Under the supervision of her advisor Professor Vladimir Bajic, she completed her thesis on developing novel computational methods to predict drug-target interactions and their functional effects using data integration, graph mining and machine learning approaches. Currently, Rawan is working as a postdoctoral associate in the Carter Lab at the Jackson Laboratory for Genomic Medicine in Farmington, Connecticut. In

Olaa A. Motwalli

Research Interests Olaa Motwalli is a Ph.D. student in Computational Science and a member of the Computational Bioscience Research Center (CBRC) at KAUST. She obtained her Bachelor's degree in Computer Science at King Abdul Aziz University in Jeddah and obtained her Master's degree in Computer and Software Engineering at Widener University in the USA. Her research interests are mainly the development and computational support for the design of sustainable biofuel production via microbial cell factories through machine learning, graph mining, and algorithm development, genome-scale metabolic

Karim Awara

Karim Awara obtained his Master's Degree in Computer Science under the supervision of Professor Panagiotis Kalnis from King Abdullah University of Science and Technology. Research Interests Karim's research interests included the Database and High-Performance Computing field. Awards and Distinctions Awarded, internationally, as one of the best 5 teams in SIGMOD 2010 contest for developing high multi-query optimization techniques. And presented the work at SIGMOD conference, June 2010 in Indianapolis, IN, USA. Rewarded as one of the top four teams in Egypt in a software Engineering competition
<> image processing (7)

Yahya Bokhari

Postdoctoral Research Fellow, Computer Science
Yahya Bokhari is a Postdoctoral Research Fellow at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST), working with Professor Wolfgang Heidrich on enhancing X-ray images under Smart Health Initiative. Education and Early Career Yahya Bokhari received his bachelor's degree in Medical Technology from King Abdulaziz University, KSA. He holds a Master's degree in Bioinformatics and a Ph.D. degree in Computer Science from Virginia Commonwealth University, USA. Research Interest Dr. Yahya Bokhari is mainly interested in Image Processing, Computer Vision

Sara Shaheen

Sara Shaheen was a Ph.D. student in Computer Science under the supervision of Professor Bernard Ghanem at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). She is currently an Assistant Professor in Computer Science at Prince Sultan University (PSU) in Riyadh, Saudi Arabia. Education and Career Sara received her Bachelor degree in Computer Science from Prince Sultan University (PSU) in Riyadh in 2010. She joined the MS/PhD program in KAUST and earned her Master and Ph.D. in Computer Science in 2017. She was a part of Image and Video Understanding

Renmin Han

Postdoctoral Research Fellow, Structural and Functional Bioinformatics
Renmin Han is a Research Scientist at the Structural and Functional Bioinformatics Group (SFB) of Professor Xin Gao at King Abdullah University of Science and Technology (KAUST). ​Renmin obtained his Ph.D. degree in Computer Science from the Institute of Computing Technology, Chinese Academy of Sciences, China. Research Interests ​Renmin is interested in the development of computer science. Currently, he is working on imaging processing in electron micrography, especially for the reconstruction of biological ultrastructures. His work is to use computational methods to improve the boundary of

Ramzi Idoughi

Research Scientist, Computer Science
Ramzi Idoughi is a Research Scientist in the Computational Imaging Group (VCCIMAGING) working with Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Ramzi obtained his engineering degree in Space System and Image Processing from National Higher French Institute of Aeronautics and Space (ISAE/SUPAERO) in France in 2011. Later, he received his master degree in Signal, Image, Acoustics and Optimization (SIAO) from Higher Educational Institution in Toulouse (ENSEEIHT) in France in 2011. After that in 2015, he received his doctoral

Michael Birsak

Research Scientist, Computer Science
Michael Birsak is a Research Scientist working with Professor Peter Wonka at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST).

Islam Almasri

Islam Almasri obtained his Master's degree in Computer Science under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Currently, Islam is a Ph.D. candidate in the Computer Science program under the supervision of Prof. Nina Fedoroff. at Fedoroff Research Group. Islam is specialized in developing various software tools for solving problems in other scientific domains, including Image Processing, Medical Imaging, Chemical Networks, and Protein Structure. Selected Publications Almasri, Islam

Abdulwahab Felemban

Ph.D. Student, Electrical and Computer Engineering CEMSE, King Abdullah University of Science and Technology (KAUST),completed MSc under the supervision of Prof. Tareq Al-Naffouri.
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Security, Privacy, and Human

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B4 L5 R5209

Security and privacy systems are often composed of complex components and details. However, users’ experience shouldn’t be as complex. In this seminar, Eman will discuss the human factor in the security and privacy chain. While human privacy perceptions and behaviors have been investigated in Western societies, little is known about these issues in non-Western societies.

Safeguarding the right to be forgotten

1 min read ·
An open-source software can help align artificial intelligence applications in healthcare with data privacy regulations.

Zihang Xiang

Ph.D. Student, Computer Science
Zihang Xiang is a Ph.D. student at ‌Privacy-Awareness, Responsibility and Trustworthy Laboratory under the supervision of Professor Di Wang at King Abdullah University of Science and Technology (KAUST). Publications Z. Xiang, M. M. Khan, U. Khalid and J. Xu, ”A DC link capacitors’ voltage balancing method in unidirectional cascaded H-bridge converter using multi-limb-core (MLC) transformers,” 2017 China International Electrical and Energy Conference (CIEEC),Beijing, 2017, pp. 606-611. U. Khalid, M. M. Khan, Z. Xiang and Y. Jianyang, ”Bidirectional modular Dual Active Bridge (DAB) converter

Yulian Wu

Ph.D. Student, Computer Science
Yulian Wu is a Ph.D. student under the supervision of Professor Francesco Orabona at King Abdullah University of Science and Technology (KAUST). Publications Youming Tao*, Yulian Wu*, Peng Zhao and Di Wang. "Optimal Rates of (Locally) Differentially Private Heavy-tailed Multi-Armed Bandits." https://arxiv.org/abs/2106.02575. (Equal contributions) Education Profile M.S. in Statistics, East China Normal University, China B.S. in Mathematics and Applied Mathematics, East China Normal University, China

Xiaojie Zhu

Xiaojie Zhu is a staff research scientist at KAUST. Prior to that, he was an assistant professor of cybersecurity in the Department of Computer Science and Information Technology, College of Engineering, Abu Dhabi University. He received his Ph.D. from the Department of Computer Science, University of Oslo, Norway. Xiaojie's research interests are data security and privacy, software security, decentralized trust and infrastructure, and applied cryptography. He has published in prestigious journals such as IEEE TDSC, IEEE TPDS, JNCA, and JOS. One of his articles was included in the 21st annual

Lijie Hu

Ph.D. Student, Computer Science
Lijie Hu is a Ph.D. student at ‌Privacy-Awareness, Responsibility and Trustworthy Laboratory under the supervision of Professor Di Wang at King Abdullah University of Science and Technology (KAUST). Publications Lijie Hu, Shuo Ni, Hanshen Xiao and Di Wang. "High Dimensional Differentially Private Stochastic Optimization with Heavy-tailed Data". https://arxiv.org/abs/2107.11136. To appear in the 41st ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems (PODS 2022). Di Wang*, Jiahao Ding*, Lijie Hu, Zejun Xie, Miao Pan and Jinhui Xu. "Differentially Private Expectation

Di Wang

Assistant Professor, Computer Science
Professor Wang's research addresses issues and societal concerns arising from machine learning, particularly in the areas of privacy, security, safety, fairness, robustness, interpretability and transparency. He aims to develop provable and practical algorithms to address trustworthiness issues in machine learning.
<> resilience (7)

Assessing DNS Resilience and Robustness

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B4 L5 R5220.

The Domain Name System (DNS) is a core protocol for the Internet. It resolves mappings between Internet Protocol (IP) addresses and their corresponding Fully Qualified Domain Names (FQDNs). Since all Internet communications rely on it, DNS structuring should therefore be resilient and robust against failure to avoid any service interruption. While the research community and experienced practitioners have established best practices to this end, many worldwide DNS implementations are still prone to many types of configuration errors. In this talk, I discuss the adoption of these approaches in some countries. Also, a case study is presented considering domains in Saudi Arabia (.sa) that illustrates the value of measuring the DNS at this scale. The results are valuable to improve the DNS infrastructure in the kingdom. Lastly, I provide recommendations to improve DNS service resilience and robustness.

RC3 Advisory Board Distinguished Lectures Workshop

RC3 Advisory Board

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B5 L5 R5220

Machine learning (ML) has witnessed remarkable advancements in recent years, demonstrating its effectiveness in a wide array of applications, including intrusion detection systems (IDS). However, when operating in adversarial environments, ML-based systems are susceptible to a range of attacks.

KAUST SENTRY team wins 2021 Cybersecurity Games & Conference Embedded Security Challenge

1 min read ·
The SENTRY Lab team wins the 18th annual CSAW Cybersecurity Games & Conference’s (CSAW '21) Embedded Security Challenge (ESC).

Blocking microgrid cyberattacks to keep the power flowing

1 min read ·
Power grids have become far more complex in recent decades due to energy demands, environmental regulations and small-scale renewable energy systems that turn businesses and individuals into combined consumer-producers. One way to ensure that power supplies remain resilient is to create small groups of sources and loads called microgrids. Microgrids can operate independently of the main grid when required, such as supporting hospitals during natural disasters, for example. As microgrids grow more complex, they require sophisticated computer networks to coordinate, control and distribute

Rehana Yasmin

Rehana Yasmin is a Research Scientist at the Resilient Computing and Cybersecurity Center (RC3), KAUST, since August 2023. Before joining KAUST, she had been working as a researcher in the area of Automotive Cybersecurity and Resilience at Huawei International. Prior to above, she served as a lecturer at University of Azad Jammu and Kashmir, and later as an assistant professor at Mirpur University of Science and Technology, in Pakistan. She also obtained a short experience of postdoc from University of Padova, Italy. Research Interests Yasmin's research interests lie in security and privacy

Ines Pinto Gouveia

Postdoctoral Research Fellow, Cyber Resilience Research Group
Working towards building safe, secure and resilient systems.

Ali Shoker

Senior Research Scientist, Computer Science
I am a Senior Staff Scientist, working on Cyber Security and Resilience. My research is focused on studying and using Byzantine and Intrusion Fault tolerance, Hardware (FPGA), Artificial Intelligence, and Blockchain, for critical applications like Autonomous Vehicles, Small Satellite constellations, SOC, and Fintech. I love and plan to build practical things that manifest in the society.
<> scientific computing (7)
Georgios Grekas

Georgios Grekas

Dominik L. Michels receives the first Procter & Gamble Faculty Award

1 min read ·
Dominik L. Michels, Computer Science and Mathematics faculty in the Computer, Electrical and Mathematical Science and Engineering Division (CEMSE) and head of the Computational Sciences Group within the Visual Computing Center (VCC), recently received the first Procter & Gamble (P&G) Faculty Award.

PhD Defense of Tariq AlTurkestani

1 min read ·
Maximizing I/O Bandwidth for Out-of-Core HPC Applications on Heterogeneous Large-Scale Systems PhD Dissertation Defense by Tariq Alturkestani PhD Student, Computer Science Thursday, July 9, 16:00 - 17:00, https://kaust.zoom.us/j/94054511362 Contact Person: Tariq Alturkestani The execution rate of floating-point operations has typically increased by an order of magnitude every four years during the last 30 years of modern comput ing. This exponential growth in terms of computational power has benefited from the hardware technology scaling. However, memory and storage systems have not maintained

Dominik L. Michels appointed Associate Professor of Computer Science and Applied Mathematics

1 min read ·
KAUST faculty member Dominik L. Michels has recently been promoted to associate professor of computer science and applied mathematics.

Haneen Mohammed

Haneen Mohammed was a Master student in Computer Science Program under the supervision of Professor Markus Hadwiger at Visual Computing Center (VCC) at King Abdullah university of Science and Technology (KAUST). Currently, she’s preparing her doctoral in Computer Science at Columbia University in the City of New York, United States. Education and Career Haneen earned her bachelor degree in Electrical and Computer Engineering from Effat University in Jeddah in 2015. She joined KAUST and received her master degree in Computer Science in 2017. Research Interest ​Her research interests are mainly

George Turkiyyah

Professor Turkiyyah's expertise lies in high-performance and GPU computing, numerical simulation and optimization and their application to machine learning and large-scale modeling.

Dominik L. Michels

Associate Professor, Computer Science
Professor Michels is a leading computer scientist who proactively designs strategies to face upcoming digitization challenges. His outstanding research has been recognized by the scientific community within and beyond KAUST.
<> statistics (7)

Modern Techniques of Statistical Optimization for Machine Learning

Tong Zhang, Professor of Computer Science and Mathematics, HKUST

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B9 H1

Many problems in machine learning rely on statistics and optimization. To solve these problems, new techniques are needed. I will show some of these new techniques through selected machine learning problems I have recently worked on, such as nonconvex stochastic optimization, distributed training, adversarial attack, and generative models.

Finding the Face in the Crowd

1 min read ·
An algorithm that maximizes the difference between data categories achieves high accuracy in classifying faces.

Pulling rank on spatial statistics

1 min read ·
A technique that uses the power of computing could solve statistical problems cheaper and faster than current methods.

Cutting datasets down to size

1 min read ·
A powerful statistical tool could significantly reduce the burden of analyzing very large datasets.

Training agents to walk with purpose

1 min read ·
By training a search agent to make smarter exploratory decisions, relational data can be classified more accurately and efficiently.

A model for millions of locations

1 min read ·
CEMSE statisticians developed a framework which enables modeling of a range of meteorological and environmental datasets from up to 2 million locations globally.

Abdulhakim Qahtan

Abdulhakim Qahtan obtained his Ph.D. degree in Computer Science under the supervision of Professor Xiangliang Zhang at Machine Intelligence and kNowledge Engineering (MINE) Laboratory at King Abdullah University of Science and Technology (KAUST). Dissertation Title: Efficient Estimation of Dynamic Density Function with Applications in Streaming Data. First employment: Postdoctoral researcher at QCRI in Qatar. Research Interests Abdulhakim's research interests included Data mining, Statistics​​​ and Applying statistical models in machine learning. Selected Publications Basma Alharbi, Abdulhakim
<> biomedical data (6)

Neuro-symbolic systems in the life sciences

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B9 H1 R2322

Abstract The life sciences have invested significant resources in the development and application of semantic technologies to make research data accessible and interlinked, and to enable the integration and analysis of data. Utilizing the semantics associated with research data in data analysis approaches is often challenging. Now, novel methods are becoming available that combine symbolic methods and statistical methods in Artificial Intelligence. In my talk, I will describe how to generate knowledge graph embeddings for analysis of biological and biomedical data. Brief Biography Robert

Professor Robert Hoehndorf's PhenomeNET method wins at Ontology matching workshop

1 min read ·
Professor Robert Hoehndorf and Postdoctoral Fellow Miguel Angel Rodriguez Garcia are the winners of the Ontology Alignment Evaluation Initiative Challenge sponsored by Pistoia Alliance.

Professors discuss big data and human health in latest Sci-Café

1 min read ·
​Professors Takashi Gojobori, Xin Gao and Robert Hoehndorf spoke about big data and human health at the Sci-Café event held on February 27. Their talk, which was moderated by James Calvin, Vice President for Academic Affairs, aimed to answer the question, "Can Big Data Solve My Health Problems?" This event was open to all members of the KAUST community.

Arwa Bin Raies defends her thesis proposal

1 min read ·
Arwa Bin Raies, a Ph.D. candidate under the supervision of Professor Vladimir Bajic, defended her thesis proposal on May 14, 2018.

Robert Hoehndorf promoted to associate professor at KAUST

1 min read ·
KAUST faculty member Robert Hoehndorf was recently promoted from the rank of assistant professor to associate professor. Hoehndorf’s promotion caps a year-long process where the German researcher’s scientific and scholarly output was measured and evaluated by internal and external reviews.

Robert Hoehndorf

Associate Professor, Computer Science
Robert Hoehndorf is an Associate Professor of Computer Science at KAUST. His research focuses on bioinformatics, knowledge representation and reasoning, Semantic Web technologies, and neuro-symbolic methods.
<> nanovisualization (6)

KAUST Cell Visualization Summit 2019

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B9 H2 R2325

You are invited to attend the KAUST Cell Visualization Summit 2019 organized by the Nanovisualization group from the Visual Computing Center (VCC). The summit will focus on the technological advancement that can lead to an in-silico virtual and visual representation by integrating current knowledge into a unified structural and dynamic model of a living cell. Each keynote speaker will relate to one of the technological aspects (modeling, visualization, and interaction). Keynote speakers Arthur Olson Graham Johnson Anders Ynnerman For more information about the summit, please click here. If you

Nanovisualization: Depicting the Tiny World Around Us

Ivan Viola, Associate Professor, Computer Science

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KAUST

Life at micron-scale is inaccessible to the naked eye. To aid the comprehension of nano- and micro-scale structural complexity, we utilize 3D visualization. Thanks to efficient GPU-accelerated algorithms, we witness a dramatic boost in the sheer size of structures that can be visually explored. As of today an atomistic model of the entire bacterial cell can be displayed interactively. On top of that, advanced 3D visualizations efficiently convey the multi-scale hierarchical architecture and cope with the high degree of structural occlusion which comes with dense packing of biological building blocks. To further scale up the size of life forms that can be visually explored, the rendering pipeline needs to integrate runtime construction of the biological structure. Assembly rules define how a body of a certain biological entity is composed. Such rules need to be applied on-the-fly, depending on where the viewer is currently located in the 3D scene to generate full structural detail for that part of the scene. We will review how to construct membrane-like structures, soluble protein distributions, and fiber strands through parallel algorithms, resulting in a collision-free biologically-valid scene. Assembly rules that define how a life form is structurally built need to be expressed in an intuitive way for the domain scientist, possibly directly in the three-dimensional space. Instead of placing one biological element next to another one for the entire biological structure by the modelers themselves, only assembly rules need to be specified and the algorithm will complete the application of those rules to form the entire biological entity. These rules are derived from current scientific knowledge and from all available experimental observations. The Cryo-EM Tomography is on rising and shows that we can already now reach the near-atomistic detail when employing smart algorithms. Our assembly rules extraction, therefore, needs to integrate with microscopic observations, to create an atomistic representation of specific, observed life forms, instead of generic models thereof. Such models then can be used in whole-cell simulations, and in the context of automated science dissemination.

Web-based 3D Visualization

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B9 L2 H2 H2

In this seminar I will present how to create 3D computer graphics and visualization systems for the web, using WebAssmbly and WebGPU language specifications, which are new, bleeding-edge technologies. Previously, accelerated graphics on the web was based on JavaScript libraries, which is still very popular, but they do not offer detailed memory management and code optimization, necessary for systems requiring high memory load and high computational demands. WebAssembly and WebGPU can be compiled from the C++ or Rust code, which also allows the deployment of the same codebase either for web or for the desktop-based applications.

Visiting Slovenian student relishes time at KAUST

1 min read ·
KAUST visiting student Aljaz Bogataj, who spent three months in the NANOVIS research group, shared his impressions of work and life at KAUST.

Ivan Viola

Professor, Computer Science
Professor Viola’s expertise lies in scientific visualization and visual computing. His research focuses on multiscale visualization and modeling of complex biological systems, ranging from cellular organisms to atomistic-level details.

Deng Luo

Ph.D. Student, Computer Science
Deng Luo is a Ph.D. Candidate in the Computer Science program under the supervision of Professor Ivan Viola at the Visual Computer Center (VCC) at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia. Education and Early Career Deng Luo earned his Bachelor's degree in Bioinformatics from Southern University of Science and Technology in Shenzhen, China in 2016. He has a multidisciplinary background, including bioinformatics, molecular biology, computer science, and entrepreneurship. He joined KAUST and received his Master's degree in Bioscience in 2019. Deng was trained as
<> neural network (6)

Muhammad Esmat

Visiting student at, King Abdullah University of Science and Technology (KAUST).

Maxat Kulmanov defends his thesis proposal

1 min read ·
Maxat Kulmanov, a Ph.D. candidate under the supervision of Assistant Professor Robert Hoehndorf, defended his thesis proposal on July 31, 2018.

Mohammed Aljahdali

M.S. Student, Computer Science
Mohammed Khalid Aljahdali is a M.S./Ph.D. student in computer science program and member of the Software-defined Advanced Networked and Distributed Systems (SANDS) Lab under the supervision of Professor Marco Canini. Research Interests Mohammed's research interests include deep learning.

Maxat Kulmanov

Research Scientist, Bio-Ontology Research Group
Maxat Kulmanov is a Research Scientist in the Bio-Ontology Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Maxat's research interests include bioinformatics, knowledge representation and reasoning, machine learning, neural networks, semantic web, and algorithms. He is interested in knowledge discovery and data integration using artificial intelligence and semantic web technologies in biology and biomedicine. Professional Profile 2020-Present: Postdoctoral Research Fellow, Computational Bioscience Research Center, King Abdullah University of

Jun Chen

Ph.D. Student, Computer Science
Jun Chen is a Ph.D. candidate in the Computer Science Program under the supervision of Professor Mohamed Elhoseiny at King Abdullah University of Science and Technology (KAUST). Education and Early Career Jun obtained his bachelor's degree in Computer Science from Xi’an Jiaotong-Liverpool University in China, 2018. He joined KAUST as a Master student under the supervision of Prof. Robert Hoehndorf and received his master's degree in Computer Science in 2019. Research Interests Jun's research interests include machine learning, artificial intelligence, natural language processing, knowledge

Gian Marco Messa

Ph.D. Student, Computer Science
Gian Marco is a Ph.D. student at the Structural and Functional Bioinformatics Group (SFB) under the supervision of Professor Xin Gao at King Abdullah University of Science and Technology (KAUST). Gian Marco, upon finishing high school in Lecce (Italy), applied to the Politecnico of Milan in the year 2009 in Physics engineering. Due to personal issues, he came back to his hometown to complete the Bachelor's Degree at the University of Salento in Biotechnology. For his master's degree, seeking innovation and cutting edge knowledge, he moved to the University of Bologna, which offered an M. Sc
<> Ontologies (6)

Ontology Design Patterns for Combining Pathology and Anatomy: Application to Study Ageing and Longevity in Inbred Mouse Strains

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B9 R3120

Abstract In biomedical research, ontologies are widely used to represent knowledge as well as annotate datasets. Many of the existing ontologies cover a single type of phenomena, such as a process, cell type, gene, pathological entity or anatomical structure. Consequently, it is required to use multiple ontologies to fully characterize the observations in the datasets. Although this allows precise annotation of different aspects of a given dataset, it limits our ability to use the ontologies in data analysis, as the ontologies are usually disconnected and their combination cannot be exploited

AI4GH Seminar Series| Artificial Intelligence for Genomics and Health: Introduction and Overview

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B2 R5220

Abstract Genomics and health are prime areas for the development and application of novel intelligent algorithms due to a large amount of available research data, the high complexity of the problem, and the significant social and economic benefits of improving health outcomes in humans. In the past, genomics and health have been one of the key drivers in the development of Artificial Intelligence methods, including machine learning, expert systems, or graph-based algorithms. I will introduce the new seminar series on Artificial Intelligence for Genomics and Health (AI4GH), and then discuss our

AI4GH Seminar Series - Variant Prioritization in Cancer: Understanding and Prediction of Cancer Driver Genes and Mutations

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B2 R5220

Sequencing has identified millions of somatic mutations in human cancers. Identifying and distinguishing cancer driver genes amongst the millions of candidate mutations remains a major challenge.

Variant Prioritization in Cancer: Understanding and Prediction of Cancer Driver Genes and Mutations

-

B3 R5209

Abstract Sequencing has identified millions of somatic mutations in human cancers. Identifying and distinguishing cancer driver genes amongst the millions of candidate mutations remains a major challenge. Accurate identification of driver genes and driver mutations is critical for advancing cancer research and personalizing treatment based on accurate stratification of patients. Due to inter-tumor genetic heterogeneity, many driver mutations within a gene occur at low frequencies, which make it challenging to distinguish them from other non-driver mutations. Motivated by these challenges, we

Neuro-symbolic methods for Semantic Web ontologies

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B9 L2 H2 H2

Semantic Web ontologies are widely used to provide a conceptual schema for sharing and integrating data and knowledge using a logic-based language. The content of ontologies may also be used to provide background knowledge in machine learning models or provide domain-specific constraints that can be verified automatically and used for zero-shot predictions. The combination of embedding symbolic representations (such as ontologies) and extracting symbolic representations from the embeddings are two main components of neuro-symbolic AI systems. I will introduce methods for embedding Semantic Web ontologies and outline some of the properties of the embeddings that relate to model and proof theory.

Sara Althubaiti

Ph.D. Student, Computer Science
Sara Althubaiti is currently a Ph.D. candidate in the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Robert Hoehndorf. Prior to this, Sara received her master's degree in computer science with a focus on bioinformatics from KAUST in December 2018. Research Interests Sara's research interests include bioinformatics, text mining, ontologies, and cancer. Her research focuses on applying machine learning methods in cancer biology and development specifically in the field of finding driver genes and mutations in
<> Reinforcement Learning (6)

Aznaur Aliev

M.S. Student

We used Reinforcement Learning; but did it work?

Prof. Susan Murphy, Statistics and Computer Science and Radcliffe Alumnae Professor at the Radcliffe Institute, Harvard University

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B9 L2 H2

Reinforcement Learning provides an attractive suite of online learning methods for personalizing interventions in Digital Behavioral Health. However, after a reinforcement learning algorithm has been run in a clinical study, how do we assess whether personalization occurred? We might find users for whom it appears that the algorithm has indeed learned in which contexts the user is more responsive to a particular intervention. But could this have happened completely by chance? We discuss some first approaches to addressing these questions.

Inference for Longitudinal Data After Adaptive Sampling

Prof. Susan Murphy, Statistics and Computer Science and Radcliffe Alumnae Professor at the Radcliffe Institute, Harvard University

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B9 L2 H2

In this work, we proved statistical inference for the common Z-estimator based on adaptively sampled data. Adaptive sampling methods, such as reinforcement learning (RL) and bandit algorithms, are increasingly used for the real-time personalization of interventions in digital applications like mobile health and education. As a result, there is a need to be able to use the resulting adaptively collected user data to address a variety of inferential questions, including questions about time-varying causal effects.

Rihan Huang

M.S. Student, Computer Science

MD Nurul Muttakin

Ph.D. Student, Computer Science
Nurul Muttakin, is an M.S./Ph.D. candidate in the Bio-Ontology Research Group (BORG) under the supervision of Professor Robert Hoehndorf. Before joining KAUST Nurul completed his B.Sc. in CSE from Bangladesh University of Engineering and Technology, Bangladesh. Research Interest Nurul is Interested in the application of Artificial Intelligence in Bioinformatics tasks like protein function prediction as well as understanding the theory behind it. Education Profile B.Sc. in CSE, Bangladesh University of Engineering and Technology, Bangladesh

Deyao Zhu

Ph.D. Student, Computer Science
Deyao Zhu is a Computer Science Ph.D. student at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Mohamed Elhoseiny in the Vision-CAIR Group. Education and Early Career Deyao obtained his Master's degree in Electrical Engineering and Information Technology at Gottfried Wilhelm Leibniz Universität Hannover, Germany. His Master thesis is done in Max Planck Institute for Intelligent Systems which focuses on representation learning. Before that, he received his Bachelor’s degree in Mechatronics at Tongji University
<> autonomous vehicles (5)

Breaking the Vehicle Over-The-Air Update System

A modern vehicle is composed of around 100 Electronic Control Unit (ECU) connected via several types of networks. An ECU is an embedded device, similar to a RaspberryPI, running an operating system, e.g., Linux-based or real-time OS, on top of which different software and firmware may run, depending on the application. Due to the imperfection of humans, software can have faults and vulnerabilities, which can lead to catastrophic failures that threatens human lives. This makes the manufacturers liable to such failures and thus often caused millions of vehicles recalls for repair. A smart

Resilient Computing: when Dependability meets Cybersecurity

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B9 L2 H2 R2325

Computing and communications infrastructures have become commodities that transact huge quantities of data and are pervasively interconnected, inside countries, and worldwide. Modern societies largely depend on them.

Towards Cyber Resilient Autonomous Vehicle Ecosystems: Pitfalls and Enablers

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B9 L2 H2 H2

Currently, there is a substantial push towards autonomous vehicles in the market. However, fully autonomous vehicles, using extensive fault-tolerance e.g., in x-by-wire functions, are still not quite safe from an accidental faults perspective. Furthermore, they present an even greater threat surface to combined accidental and malicious faults. This pitfall has been very slowly recognized by car makers. The consequences of such accidents or attacks are likely to be severe, life-threatening included. This talk will discuss this threat surface in an analysis including the whole ecosystem.

Rehana Yasmin

Rehana Yasmin is a Research Scientist at the Resilient Computing and Cybersecurity Center (RC3), KAUST, since August 2023. Before joining KAUST, she had been working as a researcher in the area of Automotive Cybersecurity and Resilience at Huawei International. Prior to above, she served as a lecturer at University of Azad Jammu and Kashmir, and later as an assistant professor at Mirpur University of Science and Technology, in Pakistan. She also obtained a short experience of postdoc from University of Padova, Italy. Research Interests Yasmin's research interests lie in security and privacy

Erick Rodrigues E Silva

Education Erick pursued his education in Control and Automation Engineering at CEFET-RJ, where he was awarded a Motion of Praise for his victory in a national robotics championship. He authored a thesis titled "Platform for Simulation and Generation of Synthetic Data for Autonomous Inspections by UAVS in Solar Plants." Experience During the thesis composition, Erick made a significant contribution to the paper "Virtual Reality Simulation of Autonomous Solar Plant Inspections with Unmanned Aerial Systems." This paper was published at ICUAS, a conference held in Dubrovnik, Croatia in 2021. After
<> Computational Photography (5)

Humboldt Research Award for Prof. Heidrich

1 min read ·
The Alexander von Humboldt Foundation has awarded a Humboldt Research Award to Prof. Wolfgang Heidrich.

Xinge Yang

Ph.D. Student, Computer Science
Xinge Yang is a computer science PhD student at KAUST, working on computational optics and computational imaging.

Wolfgang Heidrich

Professor, Computer Science
Professor Heidrich is apinoeer in the development of novel sensing and display technologies by combining computer graphics, machine vision, imaging, inverse methods, optics and perception.

Masheal Alghamdi

Masheal Alghamdi is a Ph. D. candidate in the Computational Imaging Group (VCCIMAGING) under the supervision of Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Masheal obtained her bachelor degree in Computer Science from King Abdul-Aziz University (KAU) in Jeddah, Saudi Arabia in 2009. After that she joined KAUST to receive her master degree in Computer Science in 2014. Masheal is a researcher in the National Center for Computation Technology and Applied Math (CTAM) at King Abdul-Aziz City for Science and Technology (KACST)

Julio Rey Ramirez

Ph.D. Student, Computer Science
Julio Rey Ramirez is a Master student under the supervision of Prof. Wolfgang Heidrich at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career Julio Rey Ramirez was an electronics engineer from National University of Colombia (UNAL). He worked as laboratory assistant in the lighting testing lab (LABE) at UNAL from 2015 to 2018. Julio Rey Ramirez joined KAUST as a visiting student in 2019, and worked in the programming of machine vision cameras and computational microscopy under the supervision of professor Wolfgang Heidrich
<> Computer architecture (5)

Architectural Defenses against Indirect Threats

Yasser Shalabi, Graduate Student, Computer Science, University of Illinois Urbana-Champaign

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B9 L2 H2

The potential of indirect execution attacks – e.g. Return-Oriented-Programming and Transient-Execution based Side-channels – threaten all modern computing platforms. Standard security policies are unable to eliminate these threats. What is the role of hardware in mitigating these threats? Why are the latest processor designs no longer proactively eliminating threats? In this talk, we will explore these questions and reconsider the role of hardware in securing systems. I will present Record-and-Replay as a fundamental solution that can enable a hardware-software co-design that can strengthen the security of modern computing platforms.

Suhaib Fahmy

Associate Professor, Computer Science
Professor Fahmy’s research explores novel ways to integrate accelerated computation and communication to improve computing systems' efficiency, latency, and security.

Jian Weng

Assistant Professor, Computer Science
Jian Weng, an assistant professor of computer science at KAUST, is actively working on improving computer systems' energy efficiency and performance by reforming software and hardware interfaces.

Elmootazbellah Elnozahy

Professor (former), Computer Science
Elmootazbellah (Mootaz) Elnozahy is a renowned computer scientist who has filed, individually and with colleagues, 58 U.S. patents. He has successfully secured research and development funding grants of ~$320M.

Ahmad Tariq Sheikh

Postdoctoral Research Fellow, Computer Science
<> computer networks (5)
Syed Abidullah Fareed

Syed Abidullah Fareed

Visiting Student (former), Information Science Lab

Kamran Jamshaid

Postdoctoral Research Fellow, Networking Research Lab
Kamran Jamshaid was a Postdoctoral Research Fellow at Networking Research Lab (NETLAB) in King Abdullah University of Science and Technology (KAUST), working with Professor Basem Shihada in his research group. Research Interests Kamran's research interests included computer networks, wireless data networks, distributed systems, and pervasive and mobile computing. Selected Publications A. Showail, A. Elrasad, A. Meer, A. Daghistani, K. Jamshaid, and B. Shihada, "iFrag: Dynamic Partial Packet Recovery for Sensor Systems", ACM Journal of Wireless Networks, Vol. 20, No. 4, pp. 1-18, 2014. [ PDF] I

Haneen Mohammed

Haneen Mohammed was a Master student in Computer Science Program under the supervision of Professor Markus Hadwiger at Visual Computing Center (VCC) at King Abdullah university of Science and Technology (KAUST). Currently, she’s preparing her doctoral in Computer Science at Columbia University in the City of New York, United States. Education and Career Haneen earned her bachelor degree in Electrical and Computer Engineering from Effat University in Jeddah in 2015. She joined KAUST and received her master degree in Computer Science in 2017. Research Interest ​Her research interests are mainly

Arwa Bin Raies

Research Interests I am a computer scientist whose primary focus is on developing novel machine learning methods to extract useful information from complex datasets. My experience, however, has covered intellectually wide-ranging areas spanning several disciplines in computer science, biology, and chemistry. At the undergraduate level, I focused on computer networks, and I did an internship for six months at Cisco Systems Inc., and I achieved CCNA and CCNP certification. At the Master's level, however, I joined the Computational Bioscience Research Center. I was in charge of a text-mining

Ahmed Mohamed Abdelmoniem Sayed

Dr. Ahmed Sayed is a Postdoctoral research fellow at ECRC, working with Professor Marco Canini. He is focused on tackling challenging problems related mainly to improve the performance and understanding of machine learning systems and their usage for optimizing and automating computer and network systems. Why KAUST? KAUST is the beacon for innovation and creativity in the middle-east area and they follow the model of HKUST to become a world-wide top-tier leading university in science and technology. Why networking The intersection point between Networked Systems & Machine Learning is a very
<> COVID-19 (5)

AI that's built to save lives

1 min read ·
Associate Professor Xin Gao and his group have developed an artificial-intelligence (AI) based solution to help increase COVID-19 testing accuracy. Identifying cases of early stage infection has been particularly challenging for frontline clinicians. Gao's AI-based model, which aims to increase accuracy, has been put to immediate use at King Faisal Specialist Hospital (KFSH) in Riyadh.

Making the invisible, visible

1 min read ·
As bioscience and medicine progress, we are now able to live with diseases, which were once a death sentence. And we are rapidly discovering new interactions at the microscopic level that give us insight into the engines of life. However, as research gets more complicated, the layperson gets left further and further behind. Even those who trust the work of scientists can feel overwhelmed when attempting to read the latest research.

Studying COVID-19’s envelope protein

1 min read ·
A likeness between genes of the SARS and COVID-19 viruses could inform research into potential treatments.

How AI helps improve COVID-19 testing

1 min read ·
Following a call by President Tony Chan for KAUST PIs to contribute through their research capabilities to alleviate the COVID-19 pandemic, efforts coordinated by Donal Bradley, KAUST vice president for research, and Pierre Magistretti, KAUST dean of the Biological and Environmental Science and Engineering division, mobilized a group of faculty to form the Rapid Research Response Team (R3T).

Peering under the hood of SARS-CoV-Two

1 min read ·
Microscope and protein data are incorporated into an easy-to-use-and-update tool that can model an organism’s 3D appearance.
<> embedded systems (5)
Syed Abidullah Fareed

Syed Abidullah Fareed

Visiting Student (former), Information Science Lab

Vehicle Intrusion Resilience Systems in Action

A modern vehicle is composed of around 100 Electronic Control Unit (ECU) connected via several types of networks. An ECU is an embedded device, similar to a RaspberryPI, running an operating system, e.g., Linux-based or real-time OS, on top of which different software and firmware may run, depending on the application. Due to the imperfection of humans, software can have faults and intrusions, which can lead to catastrophic failures that threatens human lives. A Fault and Intrusion Resilient System (FIRS) is a vehicle middleware that can mask the effect of a failure or intrusion. Contrary to

Yousif Aldolaijan

Yousif Aldolaijan was a Computer Science Master student under the supervision of Prof. Ivan Viola at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Yousif received his bachelor’s degree in Computer Science at the University of Southern California (USC), Los Angeles, USA in 2018. He joined KAUST to continue his studies in Computer Science and received his Master's degree in Computer Science from KAUST in 2020. During the summer of 2018, he joined Google as a Software Engineering Intern for three months. He worked on

Moetaz Abbas

Research Engineer, Computer Science
Moetaz Abbas was an Optoelectronic Engineer in the Computational Imaging Group (VCCIMAGING) working with Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Moetaz obtained his bachelor degree in Electronics Engineering from The American University in Cairo (AUC) in Egypt in 2010. By 2011, he received his master degree in Computer Science from King Abdullah University of Science and Technology (KAUST) in Saudi Arabia. After his masters, he returned back to The American University in Cairo (AUC) as a teaching assistant for the

Charalambos (Harrys) Konstantinou

Professor Konstantinou is an expert in critical infrastructure security and resilience, focusing on smart grid technology, renewable energy integration and real-time simulation.
<> geometric modeling (5)

Victor Ceballos Inza

Ph.D. Student, Computer Science
Victor Ceballos Inza is a Ph.D. student under the supervision of Prof. Helmut Pottmann at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST).

Ibraheem Alhashim

Ibraheem Alhashim is a Remote Researcher working with Professor Peter Wonka. Education and Early Career Ibraheem Alhashim completed his B.Sc. with honors in Computer Science at the Portland State University in 2008, followed by his M.Sc. in Computer Science in 2011 under the supervision of Prof. Hao Zhang and his Ph.D. in Computer Science in 2016 under the supervision of Prof. Hao Zhang and Prof. Ghassan Hamarneh at Simon Fraser University in Canada. During both his Master and Ph.D., Alhashim was working at the GrUVi Lab at Simon Fraser University in Canada. Dr. Alhashim joined KAUST in 2017

George Turkiyyah

Professor Turkiyyah's expertise lies in high-performance and GPU computing, numerical simulation and optimization and their application to machine learning and large-scale modeling.

Chi-Han Peng

Research Scientist, Computer Science
Chi-Han Peng was a Research Scientist working with Professor Peter Wonka at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). He is an Assistant Professor at the School of Information Science and Technology (SIST) at ShanghaiTech University. Education and Early Career Before joining KAUST, Dr. Peng was a postdoctoral research associate at University College London (UCL), working with Prof. Niloy Mitra. He worked at Nogle, a private company based in Taipei, on the 3D modeling and simulation of real-world clothes for the fashion/garment industries

Caigui Jiang

Research Scientist, Computer Science
Caigui Jiang is a Research Scientist at the Visual Computing Center (VCC) working with Professor Helmut Pottmann at King Abdullah University of Science and Technology (KAUST). Education and Early Career Dr. Jiang obtained a bachelor degree in Electronic Engineering and his masters in Pattern Recognition and Intelligent Systems from Xi'an Jiaotong University (XJTU) in China. After that, he joined KAUST to receive his Ph.D. in Computer Science in 2016. Caigui has a diverse experience in the academic and research field. In 2016, he joined Max Planck Institute for Informatics as a Postdoctoral
<> HPC (5)

PhD Defense of Tariq AlTurkestani

1 min read ·
Maximizing I/O Bandwidth for Out-of-Core HPC Applications on Heterogeneous Large-Scale Systems PhD Dissertation Defense by Tariq Alturkestani PhD Student, Computer Science Thursday, July 9, 16:00 - 17:00, https://kaust.zoom.us/j/94054511362 Contact Person: Tariq Alturkestani The execution rate of floating-point operations has typically increased by an order of magnitude every four years during the last 30 years of modern comput ing. This exponential growth in terms of computational power has benefited from the hardware technology scaling. However, memory and storage systems have not maintained

KAUST scientists win prestigious Gauss Award for supercomputing excellence

1 min read ·
KAUST Ph.D. graduate Dr. Noha Al-Harthi and doctoral student Rabab Alomairy, have won the German Gauss Center for Supercomputing (GCS) Award for original research that best advances high-performance computing. This makes KAUST the first Middle Eastern institution to receive this prestigious award.

Turning your desktop to a Supercomputer with ExaGeoStatR

1 min read ·
Turning your desktop to a Supercomputer with ExaGeoStatR Download at https://github.com/ecrc/exageostatR. Remember the old times where you had to leave your R simulations running for the whole night on your desktop due to a large climate/weather dataset? ExaGeoStatR combines the user productivity of R with high performance computing linear algebra software libraries to deliver supercomputing-style environment right below your desk. ExaGeoStatR enables computing the maximum likelihood using large environmental datasets on R, while extracting performance from the underlying hardware resources

ECRC contributions to SIAM CSE21

1 min read ·
ECRC @ SIAM CSE21! Follow us @KAUST_ECRC As in previous SIAM conferences on Parallel Processing (PP) and Computational Science and Engineering (CSE), ECRC members and their collaborators will have a strong presence at SIAM CSE21, nominally in Fort Worth, Texas, but held virtually due to the pandemic. ************************************************** SIAM CSE21 will run virtually with live sessions. ************************************************** Registration to SIAM CSE is required to gain access to the online platform and attend live sessions. Once registered, please use your personal

KAUST presents exascale expertise at SC21

1 min read ·
Supercomputing experts from KAUST converged with global participants at the America's Center in St. Louis, Missouri from November 14 to 19 for SC21, the annual international conference for high performance computing (HPC), networking, storage and analysis. Established in 1988, the SC conference is the largest HPC event of its kind, with big name industries like Intel, Red Hat and Oracle sharing the exhibition floor alongside universities, government agencies and small businesses, and professionals from various affiliations attending presentations throughout the convention center. In non
<> mathematics (5)

Houcem Ben Salem

Intern (former), Information Science Lab
Research Intern at KAUST(King Abdullah University of Science and Technology) .

Useful Bitcoin Mining with a Matrix-based Puzzle

Cryptocurrency and blockchain technologies are increasingly gaining adoption since the introduction of Bitcoin, being distributed, authority-free, and secure. Proof of Work (PoW) is at the heart of blockchain’s security, asset generation, and maintenance. In most cryptocurrencies, and mainly Bitcoin, the “work” a miner must do is to solve a cryptographic puzzle: to find a random nonce that once (cryptographically) SHA-256 hashed with a perspective block header, returns a 32 Bytes number having a leading pre-defined number of zeros (called difficulty). This puzzle represents the PoW, and lives

The Discrete Charm of Geometry

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B9 L2 H2

How is modern mathematical teamwork carried out? The multiple award-winning film The Discrete Charm of Geometry by Ekaterina Eremenko will screen on April 3rd after the CEMSE Dean's Distinguished Lecture Discrete conformal mappings and Riemann Surfaces: Theory and Applications by Prof. Alexander I. Bobenko, Technische Universität Berlin. Following the screening, Prof. Bobenko will be available for a Q&A session.

Discrete conformal mappings and Riemann Surfaces: Theory and Applications

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B9 L2 H2

The general idea of discrete differential geometry is to find and investigate discrete models that exhibit properties and structures characterisitic of the corresponding smooth geometric objects. This is a challenging problem, since equivalent points of view in the smooth setting may lead to a number of inequivalent treatments in the discrete setting. We will illustrate the paradigm of structure-preserving discretizations on the example of conformal maps by showing how simple definitions lead to surprisingly rich theories.

Azimkhon Ostonov

Ph.D. Student, Computer Science
Azimkhon Ostonov is a PhD candidate at King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Mikhail Moshkov in Extensions of Dynamic Programming, Machine Learning, Discrete Optimization (TREES). Education and Early Career Azimkhon Ostonov studied at the National University of Uzbekistan in Uzbekistan. He obtained his bachelor's degree in Applied Mathematics and Informatics in 2012 and his master's degree in Computer Systems and their Software in 2014. Azimkhon started his career as a junior programmer at Fido-Biznes in Tashkent. Then, he
<> numerical simulations (5)

Dominik L. Michels receives the first Procter & Gamble Faculty Award

1 min read ·
Dominik L. Michels, Computer Science and Mathematics faculty in the Computer, Electrical and Mathematical Science and Engineering Division (CEMSE) and head of the Computational Sciences Group within the Visual Computing Center (VCC), recently received the first Procter & Gamble (P&G) Faculty Award.

Dominik L. Michels appointed Associate Professor of Computer Science and Applied Mathematics

1 min read ·
KAUST faculty member Dominik L. Michels has recently been promoted to associate professor of computer science and applied mathematics.

Juris Grigs

Ph.D. Student, Computer Science
Juris Grigs is a Computer Science Ph.D. student in the KAUST High-Performance Visualization Group (VCCVis) under the supervision of Professor Markus Hadwiger at King Abdullah University of Science and Technology (KAUST). Education and Early Career Juris obtained his bachelor's degree in ​Physics from the University of Latvia in 2017. He joined KAUST as an Electrical Engineering student with the Integrated Microwaves Packaging Antennas & Circuit Technology (IMPACT) Research Group. Now,he is a Ph.D. student in the VCCVis Research Group. Research Interests Juris's research interests include ​data

George Turkiyyah

Professor Turkiyyah's expertise lies in high-performance and GPU computing, numerical simulation and optimization and their application to machine learning and large-scale modeling.

Dominik L. Michels

Associate Professor, Computer Science
Professor Michels is a leading computer scientist who proactively designs strategies to face upcoming digitization challenges. His outstanding research has been recognized by the scientific community within and beyond KAUST.
<> real-time rendering (5)

Point cloud and volume data reconstruction and visualization

Dr. Ciril Bohak, Postdoctoral Researcher, Faculty of Computer and Information Science, University of Ljubljana, Slovenia

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B1 L2

Abstract Our ongoing research on the reconstruction of aerial point cloud data includes reconstructing the parts of the data that are not captured by the airplane mounted LiDAR system due to the shadowing effect and lack of reflections. We are developing methods for reconstructing (1) building walls, (2) reconstruction of bridges and underlying terrain; (3) reconstruction of water surfaces of lakes and rivers, and (4) reconstruction of terrain overhangs. In all the cases we are merging the point cloud data with cadaster data or digital terrain model data for filling the gaps in the point cloud

Torsten Hadrich

Ph.D. Student, Computer Science
Torsten Hadrich is a Ph.D. candidate at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Dominik Michels in Computational Sciences Group (CSG). Education and Early Career Torsten Hadrich obtained his bachelor degree in Computer Science and Media from Stuttgart Media University in Germany in 2010. After that, he received his master degree in Information Engineering from the University of Konstanz in Germany in 2014. Torsten started his career life early in 2002. He was an IT Specialist for Application

Reem Alghamdi

Ph.D. Student, Computer Science
Reem Alghamdi is a Ph.D student in VCCVIS group under the supervision of Professor Markus Hadwiger. Education Profile MS in Computer Science, King Abdullah University of Science and Technology, Saudi Arabia. B.Sc. in Computer Science, Princess Nourah bint Abdulrahman University, Saudi Arabia.

Amani Ageeli

Ph.D. Student, Computer Science
Amani Ageeli is a Ph.D. student in the Computer Science program under the supervision of Professor Markus Hadwiger at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Amani received her bachelor's degree in Computer Science from Umm Al-Qura University in Makkah, Saudi Arabia in 2016. She joined KAUST to continue her studies and earned a master's degree in Computer Science in 2019. Currently, she's working on directed research with Prof. Markus Hadwiger in the High-Performance Visualization Group. Research Interest Amani

Alberto Jaspe-Villanueva

Research Scientist, Computer Science
Alberto Jaspe-Villanueva is a Research Scientist working with Professor Markus Hadwiger at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST). Education and Early Career Alberto started developing Computer Graphics software in 2003 as an undergraduate researcher at the VideaLAB group of the University of A Coruña (Spain), where he worked for eight years on terrain rendering, virtual reality, and natural interaction. He completed his Bachelor of Computer Science in 2012, and it's Master on High-Performance Computing in 2013, at the same University
<> robotics (5)

Bridging the Gap between the Academia and Industry by Extending High-Quality Research to Practical Business Needs

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B9 L2 R2322 H1

This seminar focuses on providing the audience with the context and scope of our internship program. The program is for the young and talented graduate students with an active interest in solving real-world problems. Some of the projects that will be presented in the seminar are actively developed in Elm and include domains such as computer vision, robotics and automation, healthcare, IoT, video analytics, and NLP. The seminar will serve as a launch pad to allow students to discuss their future interests and aspirations with the speaker. It will also enable them to develop a better awareness of domains more relevant to their future research aspirations.

Best Business Plan at VIEW 2016

1 min read ·
Hachid won the award for the Venture pitch with the best business plan at the Fifth Annual Vision Industry and Entrepreneur Workshop (VIEW) 2016.

Lama Alssum

Ph.D. Student, Computer Science
Lama Alssum is a Ph.D. student in Computer Science under the supervision of Professor Bernard Ghanem in Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Lama studied Information technology at King Saud University (KSU) in Riyadh and graduated with a Bachelor degree in 2011. After her bachelor degree, she joined KSU as a Teaching Assistant. Later on, she continued her studies and earned her Master degree in Computer Science from the School of Computing Science at the University of Glasgow, United Kingdom. Lama first joined

Eric Feron

With 31 years of teaching and research experience, Professor Feron’s work utilizes elementary concepts of control systems, optimization and computer science to address critical issues in modern aerospace engineering and robotics.

Bacem Ben Nasser

Bacem Ben Nasser is a postdoctoral researcher fellow in the Electrical Engineering department within the CEMSE division. He obtained his Ph.D. degree in Sciences and Technology-Mathematics from the University of Sfax, Faculty of Sciences of Sfax, Tunisia, where he worked in Lyapunov and control theories, nonlinear integral inequalities and time scale calculus. In January 2017, he was affiliated with the University of Kairouan, Tunisia, at the Higher Institute of Applied Sciences and Technology of Kairouan as assistant professor of applied mathematics. Dr. Ben Nasser joined the research group
<> structural biology (5)

Towards rational design of biosynthesis pathways

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B3 R5209

Recent advances in genome editing and metabolic engineering enabled a precise construction of de novo biosynthesis pathways for high-value natural products. One important design decision to make for the engineering of heterologous biosynthesis systems is concerned with which foreign metabolic genes to introduce into a given host organism.

AI4GH Seminar Series - Towards Rational Design of Biosynthesis Pathways

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B2 R5220

Recent advances in genome editing and metabolic engineering enabled a precise construction of de novo biosynthesis pathways for high-value natural products. One important design decision to make for the engineering of heterologous biosynthesis systems is concerned with which foreign metabolic genes to introduce into a given host organism.

Meshari Alazami defends his thesis proposal

1 min read ·
Meshari Alazami, a Ph.D. student under the supervision of Associate Professor Xin Gao, successfully defended his thesis proposal on May 22, 2018. His topic, "Towards Rational Design of Biosynthesis Pathways", was well received by committee members Associate Professor Xin Gao, Distinguished Professor Takashi Gojobori and Assistant Professor Robert Hoehndorf.

Meshari Alazmi defends his Ph.D. thesis

1 min read ·
Meshari Alazmi, a Ph.D. candidate under the supervision of Associate Professor Xin Gao, defended his Ph.D. dissertation on October 24, 2018.

Renmin Han

Postdoctoral Research Fellow, Structural and Functional Bioinformatics
Renmin Han is a Research Scientist at the Structural and Functional Bioinformatics Group (SFB) of Professor Xin Gao at King Abdullah University of Science and Technology (KAUST). ​Renmin obtained his Ph.D. degree in Computer Science from the Institute of Computing Technology, Chinese Academy of Sciences, China. Research Interests ​Renmin is interested in the development of computer science. Currently, he is working on imaging processing in electron micrography, especially for the reconstruction of biological ultrastructures. His work is to use computational methods to improve the boundary of
<> systems biology (5)

Towards rational design of biosynthesis pathways

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B3 R5209

Recent advances in genome editing and metabolic engineering enabled a precise construction of de novo biosynthesis pathways for high-value natural products. One important design decision to make for the engineering of heterologous biosynthesis systems is concerned with which foreign metabolic genes to introduce into a given host organism.

AI4GH Seminar Series - Towards Rational Design of Biosynthesis Pathways

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B2 R5220

Recent advances in genome editing and metabolic engineering enabled a precise construction of de novo biosynthesis pathways for high-value natural products. One important design decision to make for the engineering of heterologous biosynthesis systems is concerned with which foreign metabolic genes to introduce into a given host organism.

Meshari Alazami defends his thesis proposal

1 min read ·
Meshari Alazami, a Ph.D. student under the supervision of Associate Professor Xin Gao, successfully defended his thesis proposal on May 22, 2018. His topic, "Towards Rational Design of Biosynthesis Pathways", was well received by committee members Associate Professor Xin Gao, Distinguished Professor Takashi Gojobori and Assistant Professor Robert Hoehndorf.

Meshari Alazmi defends his Ph.D. thesis

1 min read ·
Meshari Alazmi, a Ph.D. candidate under the supervision of Associate Professor Xin Gao, defended his Ph.D. dissertation on October 24, 2018.

Meshari Alazmi

As a Ph.D. student in Xin Gao's group, Meshari earned his degree in synthetic biology, systems biology, thermodynamics, and protein structure prediction. Thereafter he worked in a similar capacity in Stefan Arold's group and later went on to become an assistant professor at the University of Hail, KSA. Research Interests Meshari Alazmi's research interests are in ​Synthetic Biology, Systems Biology, Thermodynamics, Protein Structure Prediction Scientific and Professional Membership Association for Computing Machinery (ACM) student Chapter Education Profile Ph.D. in computer science, King
<> wireless sensor networks (5)

Matthew Debont

Matthew Debont obtained his Master's degree in Computer Science under the supervision of Professor Basem Shihada at King Abdullah University of Science and Technology (KAUST). Research Interests Matthew's research interests included Wireless Sensor Networks, Computer Networking, Agile Software Development, and Information and Communication Technology. Thesis title: " Event Localization in Underwater Wireless Sensor Networks using Monitoring Courses." Matthew's work contributes to improving the UK's evidence-based policies in the environmental sector. More specifically, his current project

Chun Pong Lau

Chun Pong Lau obtained both his M.S. degree and Ph.D. degree from Computer Science at King Abdullah University of Science and Technology (KAUST). His Ph.D. thesis supervisor was Prof. Basem Shihada. Currently, Chun is working as a Researcher at Huawei, Hong Kong. Education Profile Ph.D., Computer Science, KAUST, Saudi Arabia M.Sc., Computer Science, KAUST, Saudi Arabia B.Sc., Computer Engineering, HKUST, Hong Kong Scientific and Professional Membership ​​​​IEEE, Student Member Research Interests Chun's research interests included video multicasting and broadcasting, superposition coded

Anas Daghistani

Anas Daghistani obtained his Master's Degree in Computer Science under the supervision of Prof. Basem Shihada from King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia. Anas received a B.Sc. degree in Computer Engineering from Umm Al-Qura University, Makkah, Saudi Arabia. Currently, he is a Ph.D. student at Purdue University. Master Thesis title: " Green-Frag: Energy Efficient Frame Fragmentation Scheme for Wireless Sensor Networks". Research Interest Anas's research interests cover a wide range of topics in computer networking such as wireless sensors, MIMO systems

Ammar Meer

Ammar Meer obtained his Master's degree in Computer Science under the supervision of Professor Basem Shihada at King Abdullah University of Science and Technology (KAUST). Thesis title: " H-Seda: Partial Packet Recovery with Heterogeneous Block Sizes for Wireless Sensor Networks."

Ahmad Al-Showail

KAUST alumnus Ahmad Showail has recently been appointed dean of the College of Computer Science and IT at the new non-profit university in Madinah, University of Prince Mugrin ( UPM). Showail graduated from the computer engineering department with honors from King Fahd University of Petroleum and Minerals ( KFUPM) in 2005. The Madinah-based alumnus started his career as a computer engineer in the Saudi Basic Industries Corporation (SABIC) before developing an interest in academia. This led him to teach at Yanbu University College at the Royal Commission in Yanbu before joining KAUST in 2009 as
<> 3D reconstruction (4)

Peter Wonka's new query tool, called a relation-augmented image descriptor will change forever querying images online.

1 min read ·
Today, querying the massive amounts of images available in online databases such as Instagram can be a time-consuming experience. Researchers from the King Abdullah University of Science and Technology (KAUST) and the University College London, have developed a new tool that generates image queries based on a geometric description of objects in spatial relationships with potential applications in computer graphics, computer vision and automated object classification.

Jinhui Xiong wins Best Paper Award at VMV 2019

1 min read ·
Fourth-year computer science Ph.D. student Jinhui Xiong won the best paper award at this year’s 24th International Symposium on Vision, Modeling, and Visualization (VMV) 2019.

Jinhui Xiong

Ph.D. Student, Computer Science
Jinhui Xiong is a Ph. D. candidate in the Computational Imaging Group (VCCIMAGING) under the supervision of Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Jinhui Xiong obtained his bachelor degree in Electronic Engineering from City University of Hong Kong (CityU) in Hong Kong in 2015. Then, he joined KAUST and received his masters in Computer Science in 2017. During his bachelor study, he spent two years on CUDA programming for various projects in Dr. Jun Fan’s lab. In 2014, he worked as an assistant researcher at EMC

Ibraheem Alhashim

Ibraheem Alhashim is a Remote Researcher working with Professor Peter Wonka. Education and Early Career Ibraheem Alhashim completed his B.Sc. with honors in Computer Science at the Portland State University in 2008, followed by his M.Sc. in Computer Science in 2011 under the supervision of Prof. Hao Zhang and his Ph.D. in Computer Science in 2016 under the supervision of Prof. Hao Zhang and Prof. Ghassan Hamarneh at Simon Fraser University in Canada. During both his Master and Ph.D., Alhashim was working at the GrUVi Lab at Simon Fraser University in Canada. Dr. Alhashim joined KAUST in 2017
<> blockchain (4)

Useful Bitcoin Mining with a Matrix-based Puzzle

Cryptocurrency and blockchain technologies are increasingly gaining adoption since the introduction of Bitcoin, being distributed, authority-free, and secure. Proof of Work (PoW) is at the heart of blockchain’s security, asset generation, and maintenance. In most cryptocurrencies, and mainly Bitcoin, the “work” a miner must do is to solve a cryptographic puzzle: to find a random nonce that once (cryptographically) SHA-256 hashed with a perspective block header, returns a 32 Bytes number having a leading pre-defined number of zeros (called difficulty). This puzzle represents the PoW, and lives

Resilient Computing: when Dependability meets Cybersecurity

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B9 L2 H2 R2325

Computing and communications infrastructures have become commodities that transact huge quantities of data and are pervasively interconnected, inside countries, and worldwide. Modern societies largely depend on them.

Towards Secure and Scalable Blockchains

Jiangshan Yu, Senior Lecturer, Monash University, Australia

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B2 B3 A0215

Over the last decade, we have witnessed a rapid growth of blockchain technologies and their applications. Different governments have announced their strategy to boost the blockchain industry. For example, Chinese President Xi Jinping has recently endorsed blockchain and its potential for the Chinese economy; and the Australian Government announced that it would establish a National Blockchain Roadmap to help position Australia’s blockchain industry to become a global leader. This talk presents an overview of three of our works towards secure and scalable blockchains. I will first revisit the honest majority assumption of permissionless blockchains (AsiaCCS’21), and then present our efforts in making blockchain more scalable and secure against real-world threats.

Ali Shoker

Senior Research Scientist, Computer Science
I am a Senior Staff Scientist, working on Cyber Security and Resilience. My research is focused on studying and using Byzantine and Intrusion Fault tolerance, Hardware (FPGA), Artificial Intelligence, and Blockchain, for critical applications like Autonomous Vehicles, Small Satellite constellations, SOC, and Fintech. I love and plan to build practical things that manifest in the society.
<> complex models (4)

Reproducibility in Computer Science - Part 1

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Reproducibility in Computer Science - Part 2

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Reproducibility in Computer Science - Part 3

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B9 L3 R3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise. We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Extraction of Complex DNN Models: Real Threat or Boogeyman? - Graduate Seminar

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B9 L2 H2 H2

The success of deep learning in many application domains has been nothing short of dramatic. The success has brought the spotlight onto security and privacy concerns with deep learning. One of them is the threat of "model extraction": when a machine learning model is made available to customers via an inference interface, a malicious customer can use repeated queries to this interface and use the information gained to construct a surrogate model. In this talk, I will describe our work in exploring whether model extraction constitutes a realistic threat. I will also discuss possible countermeasures, focussing on deterrence mechanisms that allow for the verification of ownership of ML models.
<> Continual Learning (4)

Andres Villa

Postdoctoral Research Fellow
Andres Villa is a Postdoctoral Fellow in the IVUL lab at King Abdullah University of Science and Technology (KAUST), focusing on AI for Science, Continual Learning, Visual Language Models, and Action Understanding.

Yasir Ghunaim

Ph.D. Student, Computer Science
Yasir is a Computer Science Ph.D. student in Image and Video Understanding Lab (IVUL) Group at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Bernard Ghanem.

Habib Slim

Ph.D. Student, Computer Science
Habib Slim is a Computer Science Ph.D. candidate at the Visual Computing Center (VCC)/Vison-CAIR at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Mohamed Elhoseiny Education: M.Sc Data Science, Grenoble Institute of Technology, France, 2021 M.Eng Applied Mathematics and Computer Science, Grenoble Institute of Technology, France, 2021

Deyao Zhu

Ph.D. Student, Computer Science
Deyao Zhu is a Computer Science Ph.D. student at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Mohamed Elhoseiny in the Vision-CAIR Group. Education and Early Career Deyao obtained his Master's degree in Electrical Engineering and Information Technology at Gottfried Wilhelm Leibniz Universität Hannover, Germany. His Master thesis is done in Max Planck Institute for Intelligent Systems which focuses on representation learning. Before that, he received his Bachelor’s degree in Mechatronics at Tongji University
<> Cyber attacks and defenses (4)

Finding the linchpins of the dark web: Understanding and detecting malicious infrastructure

Dr. Sumayah Alrwais, Assistant Professor, King Saud University, Riyadh, KSA

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B9 L2 H1 R2322

In this talk, Sumayah will survey a number of malicious hosting infrastructures for different services and approaches to detecting them. Among them are works on an emerging trend of Bulletproof hosting services reselling infrastructure from lower-end service providers, use of residential proxy as a service to avoid server-side blocking and DNS based hosting infrastructure.

Multi-layer Red Team vs. Blue Team Methods towards Secure Cyber-Physical Energy Systems

Charalambos (Harrys) Konstantinou, Assistant Professor of Electrical and Computer Engineering with Florida A&M University and Florida State University (FAMU-FSU) College of Engineering

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B9 L2 H1

Election hacking, power grid cyber-attacks, troll farms, fake news, ransomware, and other terms have entered our daily vocabularies and are here to stay. Cybersecurity touches nearly every part of our daily lives. Most importantly, national security and economic vitality rely on a safe, resilient, and stable cyber-space. We rely on cyber-physical systems with hardware devices, software platforms, and network systems to connect, travel, communicate, power our homes, provide health care, run our economy, etc. However, cyber-threats and attacks have grown exponentially over the past years, exposing both corporate and personal data, disrupting critical operations, causing a public health and safety impact, and imposing high costs on the economy. In this talk, we will focus on cyber-physical energy systems (CPES) as the backbone of critical infrastructure, and provide a research perspective and present red team security threats, challenges, and blue team countermeasures. We will discuss recent approaches on developing low-budget targeted cyberattacks against CPES, designing resilient methods against false data, and the need for an accurate assessment environment achieved through the inclusion of hardware-in-the-loop testbeds.

Blocking microgrid cyberattacks to keep the power flowing

1 min read ·
Power grids have become far more complex in recent decades due to energy demands, environmental regulations and small-scale renewable energy systems that turn businesses and individuals into combined consumer-producers. One way to ensure that power supplies remain resilient is to create small groups of sources and loads called microgrids. Microgrids can operate independently of the main grid when required, such as supporting hospitals during natural disasters, for example. As microgrids grow more complex, they require sophisticated computer networks to coordinate, control and distribute

Ilies Benhabbour

Ilies is working with professor Marc Dacier on the following subject: Fingerprinting of intra network semi active components in end-to-end communications. That is to say that we want to be able to identify components which may exist between two devices. It can be either something normal like a firewall or a proxy, but it can also be something malicious like a man in the middle attack. The project's goal will thus be to identify how we can do so, and what kind of experiments can be done over the packets to see if these semi active components exist. Another aim would be to create some code that
<> cyber-physical systems (4)

Resilient Cyber-Physical and Embedded Systems - Or how to cheat to get to n=f+1

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B5 L5 R5209

Our society keeps entrusting ICT systems with high value cyber-only assets, such as our most sensitive data, finances, etc. However, when it comes to cyber-physical systems and their ability to act in and with the physical world, lifes are at risk and require rigorous protection against accidental faults and cyberattacks.

Securing Cyber-Physical and IoT Systems in Smart Living Environments

Prof. Sajal K. Das is a Curators’ Distinguished Professor of Computer Science, and Daniel St. Clair Endowed Chair, Missouri University of Science and Technology, USA.

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B4 L5 R5220

Our daily lives are becoming increasingly dependent on smart cyber-physical infrastructures, such as smart homes and cities, smart grid, smart transportation, smart healthcare, smart agriculture, and so on.

Erick Rodrigues E Silva

Education Erick pursued his education in Control and Automation Engineering at CEFET-RJ, where he was awarded a Motion of Praise for his victory in a national robotics championship. He authored a thesis titled "Platform for Simulation and Generation of Synthetic Data for Autonomous Inspections by UAVS in Solar Plants." Experience During the thesis composition, Erick made a significant contribution to the paper "Virtual Reality Simulation of Autonomous Solar Plant Inspections with Unmanned Aerial Systems." This paper was published at ICUAS, a conference held in Dubrovnik, Croatia in 2021. After

Charalambos (Harrys) Konstantinou

Professor Konstantinou is an expert in critical infrastructure security and resilience, focusing on smart grid technology, renewable energy integration and real-time simulation.
<> data security (4)

Securing data against non-conventional adversaries

Prof. Mohammad Mannan, Concordia Institute for Information Systems Engineering, Concordia University, Montreal

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B4 L5 R5220.

I will discuss four related proposals: Gracewipe (coercion-resistant disk data deletion), Hypnoguard (cold-boot protection for RAM data in sleep), SafeKeeper (protecting web credentials from rougue IT admins), and Blindfold (protecting PKI private keys from human admins). While our solutions are possibly a step forward, more importantly, we highlight pitfalls of such solutions against a strong adversary.

Tackling credential abuse together

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B9 L2 H2 H2.

Despite long-ago predictions (e.g., see Bill Gates, 2004) that other user-authentication technologies would replace passwords, passwords remain not only pervasive but have flourished as the dominant form of account protection, especially at websites such as retailers that require a low-friction user experience. This talk will describe our research on methods to tackle three key ingredients of account takeovers for password-protected accounts today: (i) site database breaches, which is the largest source of stolen passwords for internet sites; (ii) the tendency of users to reuse the same or similar passwords across sites; and (iii) credential stuffing, in which attackers submit breached credentials for one site in login attempts for the same users' accounts at another.

Xiaojie Zhu

Xiaojie Zhu is a staff research scientist at KAUST. Prior to that, he was an assistant professor of cybersecurity in the Department of Computer Science and Information Technology, College of Engineering, Abu Dhabi University. He received his Ph.D. from the Department of Computer Science, University of Oslo, Norway. Xiaojie's research interests are data security and privacy, software security, decentralized trust and infrastructure, and applied cryptography. He has published in prestigious journals such as IEEE TDSC, IEEE TPDS, JNCA, and JOS. One of his articles was included in the 21st annual

Muhamad Felemban

Muhamad Felemban obtained his Master's degree in Computer Science under the supervision of Professor Basem Shihada at King Abdullah University of Science and Technology (KAUST) in 2011. Muhamad received a B.S. degree from King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia, in 2008, and a Ph.D. degree from the School of Electrical and Computer Engineering, Purdue University, in 2018. Currently, he is an Assistant Professor in the Computer Engineering department at King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia. Master's Thesis title: " Optimal Node
<> fluid dynamics (4)

Cohomology in fluids

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B4 L5 R5209

We present a topological analysis of the vorticity formulation in describing fluid dynamics. Despite its widespread use in fluid mechanics, this formulation is insufficient at describing fluid dynamics on a non-simply-connected domain. What is missing is an equation of motion for fluid's cohomology component, which exhibits fascinating dynamics previously under explored. Using geometric language, we derive the new equation of motion and establish new conservation laws, as Casimir invariants in Hamiltonian mechanics, for fluids on domains with general topology. Significantly, we present the first physically correct vortex method on curved surfaces with genus and boundaries.

Harnessing the particle-tracking power of the algorithm

1 min read ·
A simple camera system paired with a sophisticated image-processing algorithm can achieve faster and more accurate reconstructions of particle flow.
KAUST-CEMSE-Matteo-Parsani

Matteo Parsani

Professor Parsani develops robust, variable order and self-adaptive algorithms for solving fluid flow problems in complex geometries. The application domains currently driving Parsani's research are compressible computational aerodynamics, computational aeroacoustics for noise reduction in vehicles and molecular communication.

Lisandro Dalcin

Lisandro Dalcin obtained a degree in Electromechanical Engineering from National Technological University, Concepción del Uruguay, Argentina in 2001 and a PhD degree in Engineering Sciences, Computational Mechanics from National University of the Littoral, Santa Fe, Argentina in 2008. Research Interest Lisandro Dalcin's research interest include ​​Parallel and High Performance Computing, Scientific Software Development, Finite Element Methods and Computational Fluid Dynamics.​ Education Profile ​​PhD in Engineering Sciences, Computational Mechanics, National University of the Littoral, Santa
<> FPGA (4)

Rejuvenation of Diverse FPGA Softcores in a SoC

A field-programmable gate array (FPGA) is an integrated circuit designed to be reconfigured by the user after manufacturing to build a System-on-Chip (SoC) embedded device. The needed logic is usually implemented as a software image and then instantiated on the FPGA to inherit the nice properties of hardware, like higher speed and better security. Unfortunately, since the image itself, e.g., a Softcore that represents a Processing Unit, is a software, it is prone to faults and vulnerabilities that manifest after instantiation on the FPGA. Unfortunately, an Advanced Persistent Threat (APT) is

Exploring FPGAs for Virtualized In-Network Acceleration

Dr. Suhaib Fahmy, Associate Professor, Computer Engineering, University of Warwick, UK

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B2 B3 A0215

Abstract With increasing connectivity and reliance on machine intelligence to process ever-growing amounts of data, the question of how to arrange the required connectivity and computation arises. Traditional cloud computing approaches that centralize compute capability in a data center do not scale well to large scale distributed data sources that must then transmit data over constrained networks. Similarly, computing at the very edge of the network is often constrained by limited computational capacity and a lack of access to shared data. In-network computing has been proposed as a way of

Computing in the Specialization Era

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B9 L2 H2 H2

In this talk I will discuss the shift towards hardware acceleration and show with several examples from industry and from research the large role that FPGAs are playing. I will hypothesize that we are in a new era where most of the established assumptions, rules of thumb, and accumulated wisdom about many aspects of computation in general and of data processing in particular no longer hold and need to be revisited.

Ines Pinto Gouveia

Postdoctoral Research Fellow, Cyber Resilience Research Group
Working towards building safe, secure and resilient systems.
<> Graph Neural Networks (4) <> Medical imaging (4)

Karen Sanchez

Postdoctoral Research Fellow
Karen Sanchez is a Postdoctoral Researcher in the IVUL lab at KAUST, specializing in deep learning, machine learning, and artificial intelligence (AI) for healthcare applications. Her research focuses on video understanding, domain adaptation, generative AI, and methods for preserving patient privacy.

Meet KAUST prospective student: Juexiao Zhou

1 min read ·
Juexiao Zhou is a 21-year-old graduate from Shenzhen, China, who will join KAUST in the fall of 2020 as an M.S./Ph.D. candidate and member of the KAUST Structural and Functional Bioinformatics research group under the supervision of Professor Xin Gao.

Juexiao Zhou

Ph.D. Student, Computer Science
Juexiao Zhou is a Ph.D. candidate in the KAUST Structural and Functional Bioinformatics research group under the supervision of Professor Xin Gao. Before joining KAUST, Juexiao majored in Bioinformatics and graduated from the Southern University of Science and Technology (SUSTech), located in Shenzhen, Guangdong, China. Research Interests IngIntelligent Healthcare: develop machine learning (ML) / deep learning (DL) techniques for healthcare, including disease detection, risk assessment, etc. Bioinformatics: develop new methods for bioinformatics tasks, including gene regulation understanding

Islam Almasri

Islam Almasri obtained his Master's degree in Computer Science under the supervision of Professor Xin Gao at Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Currently, Islam is a Ph.D. candidate in the Computer Science program under the supervision of Prof. Nina Fedoroff. at Fedoroff Research Group. Islam is specialized in developing various software tools for solving problems in other scientific domains, including Image Processing, Medical Imaging, Chemical Networks, and Protein Structure. Selected Publications Almasri, Islam
<> network security (4)

Network Security, Internet Measurement and Threat Intelligence

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B9 L2 H2 H2

In this talk, we will show how, through a large scale Internet measurement campaign, we have been able to experimentally validate a novel technique to automatically detect malicious webscrapers bots taking advantage of so called Residential IP proxy providers. That technique has then been deployed in a real world environment and enabled us, thanks to a novel geolocalization technique, to identify malicious actors hiding behind these infrastructures, leading to actionable threat intelligence for the victims.

Marc Dacier

Professor, Computer Science
Leveraging 20+ years of international experience, Professor Dacier leads the charge in uncovering internet vulnerabilities and developing robust defenses, building a more secure digital future for all.

Mahmoud Attia

M.S. Student, Computer Science
Mahmoud Attia is currently an MS/PHD student working with the SeRBER research group led by Prof. Marc Dacier. His research interests are in network security, within the areas of IT and OT security. He previously joined KAUST as a visiting student in the summer of 2023.

Khalid A. Hakami

Khalid Hakami is a Masters student in Computer Science (CS) and a member of the Resilience Computing and Cybersecurity research Center (RC3) at King Abdullah University of Sciences and Technology. He works in network security under the supervision of Professor Marc Dacier to develop a framework that enables researchers to automate testing and fingerprinting clients based on their behavior when the server acts unexpectedly. Education and Early Career Hakami received his bachelor’s degree in Software Engineering from King Fahd University of Petroleum and Minerals (KFUPM) in 2016. In the same
<> Neural Networks (4)

Mohammed A Shukri

Technical Consultant at KAUST (King Abdullah University of Science and Technology).

Principled Scaling of Deep Neural Networks

Soufiane Hayou, Postdoc, Simons Institute, UC Berkeley

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B9 L4 R4225

Neural networks have achieved impressive performance in many applications such as image and speech recognition and generation. State-of-the-art performance is usually achieved via a series of engineered modifications to existing neural architectures and their training procedures. However, a common feature of these systems is their large-scale nature: modern neural networks usually contain Billions - if not 10's of Billions - of trainable parameters, and empirical evaluations (generally) support the claim that increasing the scale of neural networks (e.g. width and depth) boosts the model performance if done correctly. However, given a neural network model, it is not straightforward to address the crucial question `how do we scale the network?'. In this talk, I will show how we can leverage different mathematical results to efficiently scale neural networks, with empirically confirmed benefits.

Haozhe Liu

Ph.D. Student, Computer Science
Haozhe Liu is a PhD student at AI Initiative, King Abdullah University of Science and Technology (KAUST) under the supervision of Prof.Jürgen Schmidhuber.

Arnaud Dethise

Ph.D. Student, Computer Science
Arnaud Dethise is a PhD candidate in Computer Science under the guidance of Professor Marco Canini. He completed his Bachelor's degree in Engineering and Master's degree in Computer Science in 2015 and 2017 respectively, from UCLouvain, Louvain-la-Neuve, Belgium. Arnaud has a strong interest in machine learning applied to distributed systems and is currently pursuing his doctoral research in this field. His specific research interests include Neural Networks, trustworthiness, verifiability and explainability, and model privacy, with a focus on exactness and provable guarantees. Arnaud’s goals
<> parallel computing (4)

New cost-effective GPUs solver redesigns the computational game

1 min read ·
Prof. David Keyes and his team proposed a GPUs light architecture to solve systems of multiple equations involving a large amount of data. Broadly used in computer gaming, mobile, and PC graphics - GPUs allow customizing an effective result-driven support to create a computational framework for increasing the number of processors while reducing the memory required to temporarily store the data.

KAUST scientists win prestigious Gauss Award for supercomputing excellence

1 min read ·
KAUST Ph.D. graduate Dr. Noha Al-Harthi and doctoral student Rabab Alomairy, have won the German Gauss Center for Supercomputing (GCS) Award for original research that best advances high-performance computing. This makes KAUST the first Middle Eastern institution to receive this prestigious award.

KAUST prospective student: Xingdi Zhang

1 min read ·
Xingdi Zhang is a computer science graduate from the University of Electronic Science and Technology of China (UESTC), China. Xingdi will join KAUST in the fall of 2020 as a M.S./Ph.D. candidate and member of the KAUST High-Performance Visualization Group under the supervision of Professor Markus Hadwiger.

Ali Charara

Ali Charara obtained both, his MS Degree and PhD Degree from Computer Science at KAUST in 2011 and 2018 respectively. His master thesis supervisor was Alyn Rockwood and his PhD thesis supervisor was David Keyes. Ali's research interests were in High Performance Computing with a focus on parallel computing and algorithms using multicore and manycore Computer Graphics Units. Education Profile MS Degree, Computer Science, KAUST (2011) PhD Degree, Computer Science, KAUST (2018)
<> recommender systems (4)

Representation Learning for Personalized Recommendation

Prof. Xiangliang Zhang

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B9 L2 H1 R2322

Abstract Recommender systems have already been successfully used in e-commerce industry and have been influencing our daily lives. We may all have got (personalized) recommendation of news, music, movies, videos, books, restaurants, hotels, etc. This talk will introduce the recent research in my group about promoting personalized recommendation by learning representations for users/objects to recommend. Examples of applications will be given for recommendation of the next movie to watch, the interesting research papers to read, the useful datasets to explore, and interesting places to visit

Machine Learning Models for Context Aware Recommendation Systems

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B3 L5 R5209

The goal of this thesis is to pave the way towards the next generation of recommendation systems tackling such real-world challenges to improve the user experience while giving good recommendations.

Tak Man Desmond Lee

Tak Man Desmond Lee obtained his Master's degree in Computer Science under the supervision of Professor Xiangliang Zhang at Machine Intelligence and kNowledge Engineering (MINE) Laboratory at King Abdullah University of Science and Technology (KAUST). First employment: Saudi Aramco, 2011​. Research Interests Tak Man's research interests included ​Recommender systems​. Selected Publications Xiangliang Zhang, Tak Man Desmond Lee, Georgios Pitsilis, " Securing Recommender Systems against Shilling Attacks Using Social-based Clustering". Journal of Computer Science and Technology, Springer, vol.28

Basmah Altaf

Basmah Altaf obtained her Ph.D. degree in Computer Science under the supervision of Prof. Xiangliang Zhang at Machine Intelligence and kNowledge Engineering (MINE) Laboratory in King Abdullah University of Science and Technology (KAUST). Research Interests​​ Basmah's research interests include Recommender systems and Data mining​. Education Profile ​B. E., Computer & Information Systems, N.E.D. University of Engineering and Technology, Karachi, Pakistan, 2010. M. S., Computer Science, KAUST, 2013. Awards and Distinctions ​'Gold Medal' for securing '1st Position' among 140 students in Bachelor
<> resilient computing (4)

Resilient Computing: when Dependability meets Cybersecurity

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B9 L2 H2 R2325

Computing and communications infrastructures have become commodities that transact huge quantities of data and are pervasively interconnected, inside countries, and worldwide. Modern societies largely depend on them.

Defeating the attacks from bots scrapers hiding behind residential proxy IPs

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B9 L2 R2322 H1

This talks presents a very serious emerging threat: the bots scraping web sites and hiding their IPs thanks to residential IP providers. The problem, state of the art and a new solution will be explained.

Ziwu Liu

M.S. Student, Computer Science
My passion for the evolving landscape of technology has driven me to focus on the intricate interplay between resilient computing, federated learning, edge computing, and machine learning systems. My research aims to seamlessly integrate edge computing with federated learning to create resilient and adaptive machine learning systems that can operate effectively even in resource-constrained settings.

Ali Shoker

Senior Research Scientist, Computer Science
I am a Senior Staff Scientist, working on Cyber Security and Resilience. My research is focused on studying and using Byzantine and Intrusion Fault tolerance, Hardware (FPGA), Artificial Intelligence, and Blockchain, for critical applications like Autonomous Vehicles, Small Satellite constellations, SOC, and Fintech. I love and plan to build practical things that manifest in the society.
<> Semantic web (4)

Neuro-symbolic methods for Semantic Web ontologies

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B9 L2 H2 H2

Semantic Web ontologies are widely used to provide a conceptual schema for sharing and integrating data and knowledge using a logic-based language. The content of ontologies may also be used to provide background knowledge in machine learning models or provide domain-specific constraints that can be verified automatically and used for zero-shot predictions. The combination of embedding symbolic representations (such as ontologies) and extracting symbolic representations from the embeddings are two main components of neuro-symbolic AI systems. I will introduce methods for embedding Semantic Web ontologies and outline some of the properties of the embeddings that relate to model and proof theory.

Maxat Kulmanov defends his thesis proposal

1 min read ·
Maxat Kulmanov, a Ph.D. candidate under the supervision of Assistant Professor Robert Hoehndorf, defended his thesis proposal on July 31, 2018.

Maxat Kulmanov

Research Scientist, Bio-Ontology Research Group
Maxat Kulmanov is a Research Scientist in the Bio-Ontology Research Group at King Abdullah University of Science and Technology (KAUST). Research Interests Maxat's research interests include bioinformatics, knowledge representation and reasoning, machine learning, neural networks, semantic web, and algorithms. He is interested in knowledge discovery and data integration using artificial intelligence and semantic web technologies in biology and biomedicine. Professional Profile 2020-Present: Postdoctoral Research Fellow, Computational Bioscience Research Center, King Abdullah University of

Adil Salhi

Adil Salhi is a Research Engineer at Prof. Xin Gao's Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Adil obtained his Ph.D. degree in Computer Science from the University of Manchester, UK. Research Interests Adil's research interests lie in the area of data and text mining of biomedical literature. The extraction of new information from disparate structured data sources, or available information buried in free-form text, requires the deployment of a wide array of tools to overcome the challenges posed by the nature and size
<> Signal processing (4) <> software (4)

Yasser Jaber

Visiting Student Researcher ,Machine Learning Engineer at KAUST (King Abdullah University of Science and Technology).

Vulnerability discovery in binaries and protocols

Prof. Sven Dietrich, the Computer Science Department, Hunter College, the City University of New York (CUNY)

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B5 L5 R5209

Vulnerability discovery can be challenging: many software packages, both open and closed-source projects, build on existing code from public software repositories to network drives, derived from earlier versions or related software packages. Even implemented protocols rely on such repositories. It is important to detect such copies of code when the original code contains a software vulnerability, especially one that is exploitable, as seen with flaws such as the bash vulnerability Shellshock or the SSL vulnerability Heartbleed.

Efficient Computation Through Tuned Approximation

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B9 L2 R2322 H1

Numerical software is being reinvented to provide opportunities to tune dynamically the accuracy of computation to the requirements of the application, resulting in savings of memory, time, and energy. Floating point computation in science and engineering has a history of “oversolving” relative to expectations for many models. So often are real datatypes defaulted to double precision that GPUs did not gain wide acceptance until they provided in hardware operations not required in their original domain of graphics. However, computational science is now reverting to employ lower precision arithmetic where possible. Many matrix operations considered at a blockwise level allow for lower precision and many blocks can be approximated with low rank near equivalents.

Efficient Machine Learning across Hardware, Software, and Algorithms

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B2 L5 R5209

Deep neural networks (DNNs) are revolutionizing computing, necessitating an integrated approach across the computing stack to optimize efficiency. In this talk, I will explore the frontier of DNN optimization, spanning algorithms, software, and hardware. We'll start with hardware-aware neural architecture search, demonstrating how tailoring DNN architectures to specific hardware can drastically enhance performance.
<> synthetic biology (4)

Sharing expert experimental knowledge to expedite design

1 min read ·
A repository of metabolic information provides a quick reference tool for designing useful synthetic biological systems.

Meshari Alazmi

As a Ph.D. student in Xin Gao's group, Meshari earned his degree in synthetic biology, systems biology, thermodynamics, and protein structure prediction. Thereafter he worked in a similar capacity in Stefan Arold's group and later went on to become an assistant professor at the University of Hail, KSA. Research Interests Meshari Alazmi's research interests are in ​Synthetic Biology, Systems Biology, Thermodynamics, Protein Structure Prediction Scientific and Professional Membership Association for Computing Machinery (ACM) student Chapter Education Profile Ph.D. in computer science, King

Majed Alzahrani

Majed Alzahrani obtained PhD degree in Computer Science from King Abdullah University of Science and Technology in 2017. His PhD thesis supervisor was Professor Xin Gao. After graduating, he joined Saudi Aramco as a data scientist in its Drilling Data Science unit. Working in one of the largest companies in the world is a dream come true for many, for Majed Alzahrani, things couldn't be better. Research Interest Majed's research interests were in machine learning, structural bioinformatics, synthetic biology.

Hiroyuki Kuwahara

Hiroyuki Kuwahara got his Ph.D. degree in Computer Science from the University of Utah in 2008. He was then a junior researcher at Microsoft Research - Trento during 2007 and 2009. During 2009 and 2012, he was a Ray and Stephanie Lane Fellow at the School of Computer Science at Carnegie Mellon University. Since 2012, he joined the SFB group as a Research Scientist. Research Interests ​Hiroyuki's area of research is broadly in computational systems and synthetic biology. His main research focus is on the theoretical understanding of how various uncertainties contribute to the relation between
<> UAV (4)

Computer vision: Teaching computers how to see the world

1 min read ·
Ghanem works in the Visual Computing Center (VCC) and is interested in computer vision, image processing, and machine learning.

Teaching algorithms to see

1 min read ·
Four hundred hours of video are uploaded to YouTube every minute. Other than using text to search the title, description or tags associated with a video, video content search is limited. In addition to helping users find content more quickly and accurately, the ability to search video content is of paramount importance to video platforms and advertisers. Last year, YouTube was embroiled in controversy after rolling video ads for Coca-Cola and Amazon, among others, were shown before racist and extremist content. Advertising content is a major revenue stream for video platforms like YouTube

Neil Smith

Research Scientist, Computer Science
Neil Smith is a Research Scientist in the Computational Imaging Group (VCCIMAGING) working with Professor Wolfgang Heidrich at King Abdullah University of Science and Technology (KAUST). Education and Early Career Neil joined University of California in San Diego (UCSD) at the start his academic journey. He obtained his bachelor degree in Anthropology with Concentration in Archaeology in 2001. After that in 2003, he received his master degree in Anthropology. He has a doctoral in Anthropology since 2009. Dr. Smith started his career life as a Geographic Information Systems (GIS) Specialist at

Bernard Ghanem

Professor Ghanem's research focuses on computer vision and machine learning, emphasizing large-scale video understanding, vision for automated navigation and theoretical foundations.
<> AI (3) <> applied geometry (3)

Workshop on Applied Geometry and Visual Computing

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B4 L5 R5209

The “KAUST Workshop on Applied Geometry and Visual Computing” brings together leading scientists from Europe and the United States, presenting their latest results in - Applied and Discrete Differential Geometry - Geometry Processing - Computational Fabrication The talks are related to various problems in Applied Mathematics in general and to further areas of Visual Computing such as Computer Graphics, Physical Simulation and Scientific Visualization. The workshop provides a great opportunity to learn about latest developments and to discuss ongoing work with top researchers in the field.

Victor Ceballos Inza

Ph.D. Student, Computer Science
Victor Ceballos Inza is a Ph.D. student under the supervision of Prof. Helmut Pottmann at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST).

Anthony Ramos

Anthony Ramos is an MS/PhD Student at Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Helmut Pottman in his research group. Education and Early Career Anthony have obtained Bachelor's degree in Physicist from Yachay Tech University, Urcuqui, Ecuador - government project with the idea of making an international university for the country, from which he was part of the second generation of students. Research Interest Anthony Ramos is passionate about geometry and programming. He loves to bring problems to
<> combinatorial optimization (3)

Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining

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KAUST

Dynamic programming is an efficient technique to solve optimization problems. It is based on decomposing the initial problem into simpler ones and solving these sub-problems beginning from the simplest ones. A conventional dynamic programming algorithm returns an optimal object from a given set of objects. We developed extensions of dynamic programming which allow us (i) to describe the set of objects under consideration, (ii) to perform a multi-stage optimization of objects relative to different criteria, (iii) to count the number of optimal objects,(iv) to find the set of Pareto optimal points for the bi-criteria optimization problem, and (v) to study the relationships between two criteria.

Dynamic Programming Multi-Objective Combinatorial Optimization

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KAUST

In this dissertation, we consider extensions of dynamic programming for combinatorial optimization. We introduce two exact multi-objective optimization algorithms: the multi-stage optimization algorithm that optimizes the problem relative to the ordered sequence of objectives (lexicographic optimization) and the bi-criteria optimization algorithm that simultaneously optimizes the problem relative to two objectives (Pareto optimization).

New Book "Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining" of Professor Moshkov and His Former Students is Published by Springer

1 min read ·
A new book - "Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining" by Hassan AbouEisha, Talha Amin, Igor Chikalov, Shahid Hussain, and Mikhail Moshkov - was recently published by Springer.
<> combustion (3) <> computation (3)

Efficient Computation Through Tuned Approximation

-

B9 L2 R2322 H1

Numerical software is being reinvented to provide opportunities to tune dynamically the accuracy of computation to the requirements of the application, resulting in savings of memory, time, and energy. Floating point computation in science and engineering has a history of “oversolving” relative to expectations for many models. So often are real datatypes defaulted to double precision that GPUs did not gain wide acceptance until they provided in hardware operations not required in their original domain of graphics. However, computational science is now reverting to employ lower precision arithmetic where possible. Many matrix operations considered at a blockwise level allow for lower precision and many blocks can be approximated with low rank near equivalents.

Simulating evolution to understand a hidden switch

1 min read ·
Some organisms evolve an internal switch that can remain hidden for generations until stress flicks it on.

Cloud simulations get a dose of realism

1 min read ·
A focus on the fundamental physics of cloud formation leads to highly realistic simulations of different types of clouds.
<> Computer simulations (3)

Introduction to Computer Simulation - A short course - Lecture 2

Michal A. Mankowski, Assistant Professor, Erasmus School of Economics, Erasmus University Rotterdam, Netherlands

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B1 L4 R4102

This course aims to familiarize students with the Computer Simulation tools for complex problems. The course will introduce the basic concepts of computation through modeling and simulation that are increasingly being used in industry and academia. The basic concepts of Discrete Event Simulation will be introduced along with the reliable methods of random variate generation. Later in the course, the concept of simulation-based optimization will be discussed, introducing an overview of various optimization approaches. The example of simulation (and optimization) applied to design an optimal organ allocation policy in the US will be discussed. The last lecture will be devoted to the contemporary topics in simulation.

Introduction to Computer Simulation - A short course - Lecture 1

Michal A. Mankowski, Assistant Professor, Erasmus School of Economics, Erasmus University Rotterdam, Netherlands

-

B1 L4 R4102

This course aims to familiarize students with the Computer Simulation tools for complex problems. The course will introduce the basic concepts of computation through modeling and simulation that are increasingly being used in industry and academia. The basic concepts of Discrete Event Simulation will be introduced along with the reliable methods of random variate generation. Later in the course, the concept of simulation-based optimization will be discussed, introducing an overview of various optimization approaches. The example of simulation (and optimization) applied to design an optimal organ allocation policy in the US will be discussed. The last lecture will be devoted to the contemporary topics in simulation.

Introduction to Computer Simulation - A short course - Lecture 3

Michal A. Mankowski, Assistant Professor, Erasmus School of Economics, Erasmus University Rotterdam, Netherlands

-

B1 L4 R4102

This course aims to familiarize students with the Computer Simulation tools for complex problems. The course will introduce the basic concepts of computation through modeling and simulation that are increasingly being used in industry and academia. The basic concepts of Discrete Event Simulation will be introduced along with the reliable methods of random variate generation. Later in the course, the concept of simulation-based optimization will be discussed, introducing an overview of various optimization approaches. The example of simulation (and optimization) applied to design an optimal organ allocation policy in the US will be discussed. The last lecture will be devoted to the contemporary topics in simulation.
<> computing (3)