machine learning

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).

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

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

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

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

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 - 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.

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

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

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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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.

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

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.

Building SANDS at KAUST

1 min read ·
​​​​​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)

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.

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.

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.

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.

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

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.

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

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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

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.

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

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

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

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 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.

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.

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.

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

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

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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

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

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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

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.

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.

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.

Computers excel in chemistry class

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

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.

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

1 min read ·
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

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

Machine learning at speed

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

Training an AI eye on the Moon

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

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.

KAUST paper wins 2020 Computational Optimization and Applications Best Paper Award

1 min read ·
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

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.

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.

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.

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

1 min read ·
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.

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

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

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

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

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

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.