artificial intelligence

Najmeddine Dhieb

Yasmeen Alsaedy

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

Yasser Jaber

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

Syed Abidullah Fareed

Visiting Student (former), Information Science Lab

Ahmed Youssef Ragab Radwan

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

AI4GH Seminar Series | Predicting Protein Function and Phenotype Associations

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B2 R5220

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

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

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B2 L5 R5220

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

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

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B2 L5 R5209

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

VSIM: Visualization and Simulation of Genomes for Premarital Testing

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B3 L5 R5209

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

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

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B2 R5220

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

Knowledge-based methods in biomedicine

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B9 L2 H1

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

Reproducibility in Computer Science - Part 1

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B9 L3 R3223

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

Reproducibility in Computer Science - Part 2

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B9 L3 R3223

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

Reproducibility in Computer Science - Part 3

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B9 L3 R3223

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

KAUST Research Conference Digital Health 2020

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B19 L2 H1

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

Amazon Web Services (AWS) ML Immersion Day

Mohib Khan, Hesham Abouelmagd, Shijaz Abdulla (AWS)

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B19 H1

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

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

Takashi Gojobori, Distinguished Professor, Bioscience

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B9 L2 H2 R2325

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

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

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B9 L2 H2

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

Rising Stars in AI Symposium 2022 at KAUST

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B4 B5 A0215

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

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

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B9 L2 R2322 H1

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

Applied AI for sustainable development

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B9 L2 R2322 H1

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

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

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B9 L2 R2322 H1

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

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

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

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B9 L4 R4125

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

Creating Cognitive Enterprises Through Decision Intelligence

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B9 L2 H2 H2

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

The Prospect of Generative Language Processing

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B9 L2 H2 H2

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

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

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B4 L5 R5220

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

Data, AI and Cybersecurity: Synergies and Interconnection

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B9 L2 H2 H2

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

Averaging, Momentum, and Schedulers in Optimization for Deep Learning

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B9 L3 R3128

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

Computer vision: Teaching computers how to see the world

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

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

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

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

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

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

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

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

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

The awesome scope of big data

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

Sarah Alghamdi defends her master's thesis

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

Maxat Kulmanov defends his thesis proposal

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

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

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

Azza Althagafi defends her Master's thesis

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

KAUST Associate Professor Xiangliang Zhang talks about artificial intelligence

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

Tweaking tools to track tweets over time

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

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

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

AI learns complex gene-disease patterns

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

Database to support infectious disease research

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

Symbol of change for AI development

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

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

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

AI reveals nature of RNA-protein interactions

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

ActivityNet is featured in the 2019 Artificial Intelligence Index

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

LD Motif Finder locates ancient hidden protein patterns

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

AI uses your imagination

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

Approximating a kernel of truth

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

CEMSE Division startups in focus: Peregrine Genomics

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

Flattening the sentimental curve

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

Peeling back the layers of deep machine learning

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

KAUST CS student recognized with AAAI Outstanding Program Committee Award

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

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

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

AI that's built to save lives

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

KAUST Prospective Student: Siyuan Chen

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

Robert Hoehndorf promoted to associate professor at KAUST

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

How AI helps improve COVID-19 testing

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

Former KAUST VSRP student wins Best Talk Award at ISMB 2020

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

Meet KAUST prospective student: Zhenwen Liang

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

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

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

Meet KAUST prospective student: Xiaopeng Xu

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

Meet KAUST student: Konstantin Burlachenko

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

Meet KAUST prospective student: Feng Liang

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

Machine learning at speed

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

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

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

Drop the zeros for a performance boost

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

Computer learns to write captions with feeling

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

Safeguarding the right to be forgotten

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

AI tool predicts function of unknown proteins

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

Zhenwen Liang

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

Youssef S. Mohamed

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

Shahad Qathan

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

Sarah Alghamdi

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

Sakhaa Alsaedi

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

Runar Reve

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

Robert Hoehndorf

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

Othman Soufan

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

Mohamed Elhoseiny

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

Maxat Kulmanov

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

Marco Canini

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

MD Nurul Muttakin

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

Kexin Niu

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

Jürgen Schmidhuber

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

Jesper Tegner

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

Haozhe Liu

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

Feng Liang

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

Eslam Abdelrahman BAKR

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

Dimitrios Kleftogiannis

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

Deng Luo

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

Chi-Han Peng

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

Carlos Hinojosa Montero

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

Bernard Ghanem

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

Azza Althagafi

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

Amin Allam

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

Ahmed El-Roby

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

Ahmed Abdulmajeed Alabdulkarim

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