Artificial Intelligence and Machine Learning

Professor Bernard Ghanem is the Chair of the KAUST Center of Excellence for Generative AI (GenAI) and a leading expert in computer vision and machine learning. He is a professor of Electrical and Computer Engineering (ECE) and the principal investigator of the Image and Video Understanding Lab (IVUL).

Ghanem's research focuses on computer vision and machine learning, particularly on large-scale video understanding, 3D scene comprehension and the foundation of machine learning.

At KAUST, Professor Ghanem's work bridges academic innovation and industry needs, advancing AI technologies through interdisciplinary collaborations. As Chair of the KAUST Center of Excellence for Generative AI, he leads efforts to establish world-leading excellence in GenAI research by developing the next generation of models that are efficient, trustworthy and tailored for widespread deployment.

His work supports solutions for the Kingdom's national Research, Development, and Innovation (RDI) priorities—Health and Wellness, Sustainability and Essential Needs, Energy and Industrial Leadership, and Economies of the Future—while accelerating the adoption of GenAI through translational research and talent development in collaboration with industry partners.

Professor Ghanem earned his Ph.D. in Electrical and Computer Engineering in 2010 and his M.Sc. in 2008, both from the University of Illinois at Urbana-Champaign (UIUC), U.S. He served as a graduate research assistant at the Computer Vision and Robotics Lab (CVRL) at the Beckman Institute for Advanced Science and Technology at UIUC.

Professor Ghanem’s research interests and expertise lie in:

1. Robust, large-scale video understanding, including object tracking, activity recognition/detection, and retrieval.
2. Visual computing for automation, including 3D object detection, 3D tracking, 3D indoor and outdoor navigation, and Sim2Real transfer learning.
3. Development and analysis of foundational tools in computer vision and machine learning, including deep graph neural networks, neural network robustness and certification (Trustworthy AI), continual learning, and foundational models in vision and language.

Jürgen Schmidhuber is the co-chair of the Center of Excellence for Generative AI (GenAI) at KAUST and a professor in the Computer Science Program within the Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division. Before joining KAUST, he served as the Director of the Swiss AI Lab, IDSIA, and was a professor of Artificial Intelligence at the University of Lugano (USI) from 2009 to 2021.

Dr. Schmidhuber earned his Ph.D. in Computer Science from the Technical University of Munich (TUM), Germany, in 1991. He is a co-founder and chief scientist of NNAISENSE and has authored over 350 peer-reviewed papers. He is a recognized keynote speaker and adviser on AI strategies to various governments.

His pioneering work in deep learning neural networks has significantly impacted AI, with applications in speech recognition, machine translation, and personal assistants like Apple’s Siri and Amazon’s Alexa. His research group was the first to achieve superhuman performance in official computer vision contests and won a medical imaging contest in 2012.

At KAUST, Professor Schmidhuber collaborates on AI research projects, contributes to developing AI-related educational programs, and engages with public and private sector organizations in Saudi Arabia and globally.

Professor Schmidhuber is a founding leader in artificial intelligence (AI) and machine learning. At KAUST, he leads and works with many current faculty members with research interests in AI. 

He spearheads the research focus on AI applications across various fields, including health care, drug design, chemistry, materials science, speech recognition, natural language processing, automation, robotics and soft robotics.

Before joining KAUST in 2017, Peter Richtárik obtained a Mgr. in Mathematics ('01) at Comenius University in his native Slovakia. In 2007, he received his Ph.D. in Operations Research from Cornell University, U.S., before joining the University of Edinburgh, U.K., in 2009 as an Assistant Professor at the university's School of Mathematics.

The Professor of Computer Science at KAUST is affiliated with the Visual Computing Center and the Extreme Computing Research Center at KAUST.

A number of honors and awards have been conferred on Dr. Richtárik, including the EUSA Award for Best Research or Dissertation Supervisor (Second Prize), 2016; a Turing Fellow Award from the Alan Turing Institute, 2016; and an EPSRC Fellow in Mathematical Sciences, 2016. Before joining KAUST, he was nominated for the Chancellor’s Rising Star Award from the University of Edinburgh in 2014, the Microsoft Research Faculty Fellowship in 2013, and the Innovative Teaching Award from the University of Edinburgh in 2011 and 2012.

Several of his papers attracted international awards, including the SIAM SIGEST Best Paper Award (joint award with Professor Olivier Fercoq); the IMA Leslie Fox Prize (Second prize: M. Takáč 2013, O. Fercoq 2015 and R. M. Gower 2017); and the INFORMS Computing Society Best Student Paper Award (sole runner-up: M. Takáč). Richtárik is the founder and organizer of the "Optimization and Big Data" workshop series. He has given more than 150 research talks at conferences, workshops and seminars worldwide.

He was an Area Chair for ICML 2019 and a Senior Program Committee Member for IJCAI 2019. He is an Associate Editor of Optimization Methods and Software and a Handling Editor of the Journal of Nonsmooth Analysis and Optimization.

Professor Richtárik’s research interests lie at the intersection of mathematics, computer science, machine learning, optimization, numerical linear algebra, high-performance computing and applied probability.

His recent work on randomized optimization algorithms—such as randomized coordinate descent methods, stochastic gradient descent methods, and their numerous extensions, improvements and variants)—has contributed to the foundations and advancement of big data optimization, randomized numerical linear algebra and machine learning.

Professor Francesco Orabona is a leading researcher in parameter-free online optimization. He joined KAUST from Boston University's Department of Electrical & Computer Engineering. Orabona earned his B.Sc. and M.S. in electrical engineering in 2003 from the University of Naples "Federico II", Italy, and his Ph.D. in electrical engineering in 2007 from the University of Genoa, Italy. 

Prior to joining KAUST, he held positions at several institutions including, Stony Brook University, Yahoo Research, the Toyota Technological Institute at Chicago (TTIC), the University of Milan and the Idiap Research Institute in Switzerland.

He has served as an area chair for several leading conferences, including the Conference on Neural Information Processing Systems (NeurIPS), the International Conference on Machine Learning (ICML), the Conference on Learning Theory (COLT) and the International Conference on Learning Representations (ICLR). Since 2022, he has been an associate editor of the IEEE Transactions on Information Theory.

Professor Orabona's research combines practical and theoretical machine learning approaches. His research interests encompass online learning, optimization and statistical learning theory.

In his current research, he is researching "parameter-free" machine learning algorithms that function effectively without the use of expensive hand-tuned parameters.

Mohamed Elhoseiny is an associate professor in the Computer Science Program at KAUST and the principal investigator of the KAUST Vision-CAIR Research Group. He joined the CEMSE Division at KAUST in 2019, bringing extensive experience from roles including a visiting faculty position at Baidu Research and a postdoctoral research stint at Facebook AI Research from 2016 to 2019. He also held research positions at Adobe Research from 2015 to 2016 and at SRI International in 2014.

Elhoseiny earned his Ph.D. in 2016 from Rutgers University, Canada, and his B.Sc. and M.Sc. in Computer Systems from Ain Shams University, Egypt, in 2006 and 2010, respectively.

His work has received numerous recognition, including the Best Paper Award at the 2018 European Conference on Computer Vision (ECCV) Workshop on Fashion, Art, and Design for his research "DesIGN: Design Inspiration from Generative Networks." He also received the Doctoral Consortium Award at the 2016 Conference on Computer Vision and Pattern Recognition (CVPR) and an NSF Fellowship for his "Write-a-Classifier Project" in 2014. His research on creative art generation has been featured in New Scientist Magazine and MIT Technology Review, which also highlighted his work on lifelong learning.

Professor Elhoseiny’s contributions extend to zero-shot learning, which was featured at the United Nations, and his creative AI work was highlighted in HBO’s Silicon Valley. He has served as an area chair at CVPR 2021 and the International Conference on Computer Vision (ICCV) 2021, and has organized workshops at ICCV in 2015, 2017, and 2019, and at CVPR in 2021.

He has been involved in several pioneering works in affective AI art creation and has authored or co-authored numerous award-winning papers.

Elhoseiny’s primary research interests are in computer vision—the intersection between natural language and vision and computational creativity—particularly efficient multimodal learning with limited data and vision and language. He is also interested in affective AI, especially understanding and generating novel visual content, such as art and fashion.

Di Wang is an assistant professor of Computer Science and the principal investigator of the KAUST Provable Responsible AI and Data Analytics (PRADA) Lab.

Before joining KAUST, he obtained his Ph.D. in Computer Science and Engineering ('20) from the State University of New York (SUNY) at Buffalo, U.S.; a M.S. in Mathematics ('15) from the University of Western Ontario, Canada; and a B.S. in Mathematics and Applied Mathematics ('14) from Shandong University, China.

Professor Wang’s research interests include machine learning (ML), security, theoretical computer science and data mining. His overall research focuses on solving issues and societal concerns arising from ML and data mining algorithms, such as privacy, fairness, robustness, transferability and transparency.

His PART team develops accurate learning algorithms that are equally private, fair, explainable and robust. These algorithms are supported by rigorous mathematical and cryptographic guarantees.

His research includes three perspectives: theory, practice and system. The theoretical component of his work provides rigorous mathematical guarantees for PART’s algorithms. The practical part develops trustworthy learning algorithms for biomedical, health care, genetic and social data, with a final focus on deploying trustworthy learning systems for healthcare and other applicable industries.

Dr. Gao received his B.A. in Computer Science in 2004 from Tsinghua University, China, and his Ph.D. in Computer Science in 2009 from the David R. Cheriton School of Computer Science at the University of Waterloo, Canada. Before joining KAUST, he served as a Lane Fellow at the Lane Center for Computational Biology at Carnegie Mellon University, U.S., from 2009 to 2010.

He is the Associate Editor of numerous journals, including Bioinformatics, npj Artificial Intelligence, Journal of Translational Medicine, Genomics, Proteomics & Bioinformatics, Big Data Mining and Analytics, BMC Bioinformatics, Journal of Bioinformatics and Computational Biology, Quantitative Biology, Complex & Intelligent Systems, and the International Journal of Artificial Intelligence and Robotics Research.

Gao has co-authored more than 400 research articles in bioinformatics and AI and is the lead inventor on over 60 international patents.

Professor Gao's research interest lies at the intersection between AI and biology/health. His research focuses on building novel computational models, developing principled AI techniques, and designing efficient and effective algorithms. He is particularly interested in solving key open problems in biology, biomedicine, health and wellness.

In the field of computer science, he is interested in developing machine learning theories and methodologies related to large language models, deep learning, probabilistic graphical models, kernel methods and matrix factorization. In the field of bioinformatics, he works on developing AI solutions to key open problems along the path from biological sequence analysis, to 3-D structure determination, to function annotation, to understanding and controlling molecular behaviors in complex biological networks, and to biomedicine and health care. He is a world-leading expert on developing novel AI solutions for challenges in biology, biomedicine, health and wellness, in particular AI-based drug development, large language models in biomedicine, biomedical imaging analysis, and omics-based disease detection and diagnostics.

Marco Canini is an associate professor in the Computer Science program at KAUST. He obtained his Ph.D. in computer science and engineering in 2009 from the University of Genoa, Italy, after spending the last year of his degree as a visiting student at the University of Cambridge, U.K.

He holds a Laurea Degree with Honors in Computer Science and Engineering from the University of Genoa. He was a postdoctoral researcher at the École polytechnique fédérale de Lausanne (EPFL), Switzerland, from 2009 to 2012. He then worked as a senior research scientist at Deutsche Telekom's Innovation Labs and the Technical University of Berlin, Germany, for one year.

Before joining KAUST, Canini was an assistant professor of computer science at the Université catholique de Louvain, Belgium. He has also held industry positions with Intel, Microsoft, and Google.

Professor Canini‘s research interests center on the principled construction and operation of large-scale networked computer systems; in particular, the development of Software-Defined Advanced Networked and Distributed Systems (SANDS).

His research spans a number of areas in computer systems, including distributed systems, large-scale/cloud computing and computer networking with emphasis on programmable networks.

Canini’s current work focuses on improving networked systems design, implementation and operation along several vital properties such as reliability, performance, security and energy efficiency.