Profiles

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.

Basem Shihada is a leading expert in computer networking and distributed systems. He is a founding professor of the Computer Science and Electrical and Computer Engineering Programs in the Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division at KAUST. He earned his Ph.D. in Computer science from the University of Waterloo, Canada. In 2009, he was appointed visiting faculty in the Department of Computer Science at Stanford University. In 2012, he was elevated to the rank of Senior Member of IEEE. His current research covers energy and resource allocation in wired and wireless networks, software-defined networking, cloud/fog computing, the Internet of Things, data networks, and underwater networks.

Professor Shihada's research expertise lies in developing cutting-edge wireless systems, where he has made groundbreaking contributions across various domains, including intelligent wireless systems, wireless underwater systems, molecular communication systems and non-terrestrial systems. His notable achievements include:

• Aqua-Fi: The creation and successful demonstration of Aqua-Fi, the world's first underwater Wi-Fi, enabling high-speed internet connectivity in aquatic environments.
• Sun-Fi: The demonstration of Sun-Fi, the world's first passive internet via building glass.

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.

Before founding the Computational Sciences Group (CSG) at KAUST, Professor Michels joined the Computer Science Department at Stanford University, U.S., after completing his postdoctoral studies at Caltech, U.S., and his B.Sc. ('11), M.Sc. ('13), and Ph.D. ('14) at the University of Bonn, Germany.

Since joining KAUST in 2016, he has established his group at the uppermost level of his scientific community. Since its formation, the CSG has developed numerous novel computational methods based on solid theoretical foundations.

The scientific community has recognized Professor Michels’ outstanding research within and beyond KAUST. In 2019, he was awarded a €1.25 million Artificial Intelligence Grant from the German State of North Rhine-Westphalia, Germany. Together with fellow KAUST Professors Mark Tester and Peter Wonka, he received a $1.05 million KAUST Competitive Research Grant in 2021. Moreover, in 2017, he was acknowledged by Procter & Gamble with their inaugural Faculty Award for his research contributions to the consumer goods industry.

Professor Michels is actively engaged in the Association for Computing Machinery (ACM) SIGGRAPH community; he served on the technical paper committees of SIGGRAPH 2022 and 2023, and SIGGRAPH Asia 2020 and 2021.

Michels is a member of the Association for Computing Machinery, the Institute of Electrical and Electronics Engineers, the London Mathematical Society and the AGYA project at the Berlin-Brandenburg Academy of Sciences and Humanities. He is a founding member of the German AI Award top-class jury.

As an alumnus of the German Academic Scholarship Foundation, Michels leads its KAUST partnership program. He was recently inaugurated into the Göttingen Academy of Sciences and Humanities and has been listed among the German business magazine Capital's "Top 40 below 40."

As the head of KAUST's CSG, Michels’ research activities focus on fundamental and applied aspects of computational mathematics and physics to overcome practical problems in scientific and visual computing.

At present, the group addresses a broad range of topics related to algorithmics, artificial intelligence, machine learning, computer graphics, physics-based modeling, differential equations, mathematical modeling and numerical analysis.

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.

Ivan Viola received an M.Sc. in Computer Science (Dipl.-Ing.) in 2002 and a Ph.D. in Computer Science (Dr. techn.) in 2005 from TU Wien, Austria. In 2006, he joined the University of Bergen (UiB), Norway, as a postdoctoral researcher and contributed to the establishment of a new visualization research group at UiB’s Department of Informatics.

In 2008, Viola was promoted to associate professor and, in 2011, to full professor at the University of Bergen (UiB). During this period, he also served as a scientific adviser at the Christian Michelsen Institute in Norway.

Throughout his career, he has received numerous honors and recognitions for his contributions to computing visualization, including the Austrian Computer Graphics Award in 2016, the Eurographics Dirk Bartz Prize for Visual Computing in Medicine in 2013 and three Best Paper Honorable Mention awards at the IEEE VIS conference.

Aside from serving as an area or program chair at conferences such as the IEEE Visualization Conference, EuroVis, and Eurographics, Viola has been a reviewer and IPC member for several conferences in computer graphics and visualization. He was an associate editor of the Computer Graphics Forum journal and is currently serving as an associate editor for IEEE Transactions on Visualization and Computer Graphics.

In addition to co-authoring over a hundred scientific papers, he is a member of Eurographics and the IEEE Computer Society’s Visualization and Graphics Technical Community (VGTC).

Viola’s research group seeks to develop next-generation computer graphics and technologies for visualizing life forms in all scales. Focusing on scalable approaches, the research group introduces new methods to model, construct and visualize the entire complex biological cell to atomistic detail. This technology allows people to interact, explore, study and understand life at the nanoscale.

Jian Weng is an assistant professor of computer science at KAUST. Professor Weng joined KAUST from the University of California, Los Angeles (UCLA), U.S., where he completed his Ph.D. in Computer Science in 2023, advised by Professor Tony Nowatzki. He received a Bachelor of Engineering from Shanghai Jiao Tong University, China, in 2017.

Weng’s work has been recognized with an IEEE Micro Top Picks Honorable Mention, and an IEEE/ACM International Symposium on Microarchitecture (MICRO) Best Paper Runner-Up Award. 

Professor Weng’s research interests are related to hardware/software co-designed acceleration, including, but not limited to, designing and analyzing accelerators, accelerator-associated software stacks from abstraction to compiler transformations, and design automation techniques.

Weng aspires to become a long-term leader in computer science and plans to push the boundaries of full-stack computer architectures. His objective is to simplify the design flow for mainstream programmers and to implement programmable accelerators in scenarios relevant to embedded System-on-a-Chip (SoCs).

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.

Marc Dacier is a professor of Computer Science at KAUST. He is the principal investigator of the Security Research Bearing Experimental Results (SeRBER) Group. He previously served as a full professor and head of the Digital Security Department at EURECOM.

Dr. Dacier holds a Ph.D. in computer science (European Doctorate) from the Institut National Polytechnique de Toulouse, France, awarded in 1994. He has received numerous scientific awards and has served on over 120 security and dependability conference program committees.

Dacier has had a distinguished career in both academia and industry, working with several notable companies and institutions. His experience includes consulting for France Telecom and roles at IBM Research, Symantec Research Labs and the Qatar Computing Research Institute (QCRI).

At IBM, Dacier was the director of the IBM Global Security Analysis Laboratory, where his group produced the first market product for intrusion detection alert correlation. During his time at Symantec, his team developed an open platform called Worldwide Intelligent Network Environment (WINE) to share operational security data with researchers worldwide, promoting the reproducibility of security experiments. While at QCRI, he served as the founding director of the institute's cybersecurity research group.

He has served on over 120 program committees for major security and dependability conferences and has been a member of the editorial board of several top-tier peer-reviewed technical journals. In 1998, he founded the Research in Attacks, Intrusions and Defenses (RAID) conference (formerly known as Recent Advances in Intrusion Detection), which is ranked as a "Class A" conference by the Computing Research and Education Association of Australasia (CORE).

The internationally recognized expert in cybersecurity, who joined KAUST in 2021, focuses his research on intrusion detection, intrusion tolerance, network security, cybersecurity, threat intelligence and fraud detection.

At KAUST, Professor Dacier and his SeRBER group address network security issues related to the detection of middleboxes—devices that can serve a legitimate purpose in the connection between a client and a server but can also be misused by attackers to commit man-in-the-middle attacks. Another active area of research involves the security of online gaming (e-games, e-sports) and, more broadly, the metaverse. Additionally, they focus on the IoT ecosystem and operational technology (OT) networks, which are of particular interest to the oil and water industries.

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.

A founding member of KAUST, Hadwiger has published numerous scientific papers and books, including "Real-Time Volume Graphics." He has been an Assistant Professor of Computer Science from 2009 to 2014, an Associate Professor of Computer Science from 2014 to 2021, and a Full Professor of Computer Science since 2021.

Professor Markus Hadwiger’s research interests are in scientific visualization and visual computing.

Hadwiger’s investigations span a wide range of topics, including the visualization of extreme-scale data, volume visualization, flow visualization, differential geometry and mathematical physics in visualization, medical visualization, large-scale image and volume processing, multi-resolution and out-of-core techniques, domain-specific languages for visualization, interactive segmentation and feature identification and GPU algorithms and architecture.

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.

Panos Kalnis is a professor of Computer Science at KAUST. He served as chair of the University’s Computer Science program from 2014 to 2018. In 2009, Kalnis was on sabbatical at Stanford University in the United States. Before that, he was an assistant professor at the National University of Singapore.

Earlier in his career, Kalnis was involved in designing and testing very-large-scale integration (VLSI) chips at the Computer Technology Institute in Greece. He has also worked for several companies on database design, e-commerce projects, and web applications.

Kalnis received his diploma in computer engineering in 1998 from the University of Patras in Greece and his Ph.D. in 2002 from the Hong Kong University of Science and Technology (HKUST).

Kalnis served as an associate editor for the IEEE Transactions on Knowledge and Data Engineering from 2013 to 2015 and was on the editorial board of the International Journal on Very Large Data Bases from 2013 to 2017.

Kalnis' research interests include big data, cloud computing, parallel and distributed systems, large graphs, systems for machine learning. Furthermore, he is interested in computing privacy in order to advance in the fields of data mining, knowledge extraction, security and bioinformatics.

Paulo Esteves-Veríssimo is a professor in the Computer Science (CS) program at KAUST. Previously, he was a professor and FNR PEARL Chair at the University of Luxembourg's (Uni.lu) Faculty of Science, Technology and Medicine (FSTM). He also led the CritiX Research Lab at the SnT Centre at Uni.lu, which achieved world-class results and established enduring research capacity in resilient computing, cybersecurity, and dependability.

He has also been a professor and a board member of the University of Lisbon (ULisboa), Portugal. At ULisboa, he created the Navigators research group and was the founding director of Laboratório de Sistemas Informáticos de Grande Escala (LaSIGE). From its founding in 1998, the computer science and engineering lab LaSIGE has carried out research in leading-edge areas backed by key indicators of excellence.

He was UNILU-SnT’s representative at the European Cyber Security Organization (ESCO) and member of its Scientific & Technical Committee (STC). He served as Chair of the IFIP WG 10.4 on Dependable Computing and Fault-Tolerance and vice-chair of the Steering Committee of the IEEE/IFIP DSN conference. He is a Fellow of the IEEE, a Fellow of the ACM and an associate editor of IEEE Transactions on Emerging Topics in Computing (TETC).

Professor Esteves-Veríssimo is interested in architectures, middleware and algorithms for resilient modular and distributed computing. In addition to examining paradigms and techniques that reconcile security and dependability, he also explores novel applications of these paradigms and techniques. By doing so, he achieves system resilience in areas such as autonomous vehicles, distributed control systems, digital health and genomics, and blockchain and cryptocurrency.

Dr. Esteves-Veríssimo’s research has featured in over 200 peer-reviewed international publications and five international books. He has delivered over 70 keynote speeches and distinguished lectures at reputable venues. As a systems and engineering specialist, he has contributed to designing and engineering several advanced industrial prototypes of distributed, fault-tolerant, secure or real-time systems developed through research and development.

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.

Peter Wonka holds an M.Sc. in Computer Science (Dipl.-Ing.) in 1997, an M.Sc. in Urban Planning (Dipl.-Ing.) in 2002, and a Ph.D. in Computer Science (Dr. techn.) in 2001 from the Vienna University of Technology, Austria.

Before joining KAUST in 2011 as an Associate Professor of Computer Science, he worked as a postdoctoral researcher at the Georgia Institute of Technology and served as Assistant and Associate Professor at Arizona State University. He is currently a Full Professor at KAUST. He has also held roles as the CS Program Chair and Interim Director of the Visual Computing Center.

Professor Wonka is the recipient of the National Science Foundation Career Award.

Professor Wonka’s research interests lie in computer vision, computer graphics, remote sensing, and machine learning. His current research focus is deep learning, generative modeling of images, videos and 3D scenes, 3D reconstruction, 3D computer vision, and 3D vision and language.

Robert Hoehndorf is an Associate Professor of Computer Science at King Abdullah University of Science and Technology (KAUST), where he is the principal investigator of the Bio-Ontology Research Group (BORG).

Before joining the University in the fall of 2014, Professor Hoehndorf obtained his Ph.D. in Computer Science from the University of Leipzig, Germany, in 2009. Post-graduation, he spent several years in the U.K. as a research fellow and a research associate at Aberystwyth University and the University of Cambridge, respectively. He was also a postdoctoral fellow at the European Bioinformatics Institute, U.K.

Professor Hoehndorf’s main academic interests are knowledge representation, neuro-symbolic methods and their application in life sciences. He develops knowledge-based methods for analyzing large, complex and heterogeneous biological datasets and applies them to understanding genotype-phenotype relations.

His group developed the DeepGO methods for protein function prediction, neuro-symbolic methods applicable to Semantic Web ontologies and knowledge graphs, and several approaches to represent, reason over, and predict genotype-phenotype relations.

Roberto Di Pietro (Fellow, IEEE; Distinguished Scientist, ACM; Fellow, AAIA; Member Academia Europaea) is a Professor of Computer Science with the KAUST CEMSE Division, Saudi Arabia. Previously, he was a Professor in Cybersecurity and founder of the Cyber-Security Research Innovation Lab (CRI-Lab) at Hamad Bin Khalifa University (HBKU)-College of Science and Engineering (CES), Qatar.

Previously, at Bell Labs (Alcatel-Lucent/Nokia), he served as Global Head for Security Research, managing three security research departments based in Paris, Munich and Espoo, aligning research with business objectives and moving research results into innovation. Before, he was a tenured professor at the University of Padova. He started his career as a senior military officer within the Italian Ministry of Defence (MoD), working on security-related nationwide technology projects.

He has been working in the cybersecurity field for more than 25 years, leading technology-oriented and research-focused teams in the private sector, government and academia. He has served as a senior security consultant for international organizations, including the United Nations (U.N.) and U.N. agencies (the International Atomic Energy Agency (IAEA), the United Nations Global Service Centre (UNLB) and the World Intellectual Property Organization (WIPO)). In addition to his international experience, he was appointed Seconded National Expert and detached for one year at the European Union Agency for Criminal Justice Cooperation (Eurojust).

As per his drive for innovation, besides being involved in the mergers and acquisitions (M&A) of startups—and having founded one (exited)—he is on the board of research centres and startups.

In 2011-2012, he was awarded a Chair of Excellence from the University Carlos III, Madrid, Spain. In 2020, he received the Jean-Claude Laprie Award for having significantly influenced the theory and practice of Dependable Computing. In 2022, he was awarded the Individual Innovation Award from HBKU. He has been consistently included in Stanford University's "World Ranking Top 2% Scientists" list since this ranking existed.

His education accounts for an M.S. in Computer Science ('94) and an M.S. in Informatics ('03), both from the University of Pisa (UniPi), Italy, and a Specialization Diploma in Operations Research and Strategic Decisions ('03) and a Ph.D. degree in Computer Science ('04), both from the University of Rome "La Sapienza."

In his academic career, he has secured more than $9 million in funding (either as LPI or PI).

A cybersecurity expert, his main research interests include AI-driven cybersecurity, security and privacy for distributed systems (e.g., UAVs, Blockchain technology, Cloud, IoT, OSNs), applied cryptography, FinTech, Quantum Computing and data science. In particular, Di Pietro identifies three lines of research above all others: critical infrastructure protection (CIP), online social networks (OSN) and cloud security.

He has extensively contributed scientific articles to the cited topics, co-authored four books and registered many patents and applications.

Suhaib Fahmy is associate professor of Computer Science and the principal investigator of the KAUST Accelerated Connected Computing Laboratory (ACCL).

Professor Fahmy graduated from Imperial College London with an M.Eng. in Information Systems Engineering in 2003 and a Ph.D. in Electrical and Electronic Engineering in 2008. Following his Ph.D., he joined Trinity College Dublin, Ireland, as a postdoctoral research fellow and later worked as a visiting research engineer at Xilinx Research Labs Ireland, focusing on reconfigurable computing systems.

He was an assistant professor of Computer Engineering at Nanyang Technological University, Singapore, where his team led early efforts to virtualize FPGAs for cloud computing, as well as pioneering work on efficient mapping of circuits to FPGA primitives.

In 2015, he returned to the UK, joining the University of Warwick as associate professor, then Reader in Computer Engineering. While at Warwick, he led the Connected Systems Research Group and the Adaptive Reconfigurable Computing Lab and launched the joint Computer Systems Engineering degree program. He was also appointed a Turing Fellow at The Alan Turing Institute, the UK’s national institute for data science and artificial intelligence.

He has received numerous notable accolades, including the IEEE Conference on Field Programmable Technology (FPT) Best Paper Award in 2012, IBM Faculty Awards in 2013 and 2017, the UK Foreign and Commonwealth Office Collaborative Development Award in 2013, the International Conference on Field-Programmable Logic and Applications (FPL) Community Award in 2016, the ACM Transactions on Design Automation of Electronic Systems Best Paper Award in 2019, and the IEEE High Performance Extreme Computing Conference Best Paper Award in 2021.

In 2023, he was awarded the KAUST Distinguished Teaching Award for his exceptional contributions to the classroom instruction mission of the University.

Professor Fahmy and his team at the ACCL are currently investigating a variety of approaches to hardware acceleration and how connected computing can enable more efficient, performant and secure systems.

His group focuses on overcoming the inherent latency and inefficiency of existing computing abstractions. To achieve this goal, they develop connected accelerator architectures that consider connectivity from the outset alongside specialized accelerator architectures to support more challenging applications.

Wolfgang Heidrich is a professor of Computer Science and Electrical and Computer Engineering at KAUST. He is a member of the KAUST Visual Computing Center and served as its director for eight years, from 2014 to 2021. Heidrich is a pioneer in computational imaging and display, which seeks to advance imaging and display systems by co-designing optics, electronics, and algorithms.

Heidrich received his Diploma in Computer Science from the University of Erlangen-Nuremberg (FAU), Germany, in 1995, followed by an M.Math from the University of Waterloo, Canada, in 1996. He also earned a Ph.D. in 1999 from FAU.

In 2014, Heidrich was honored with a Humboldt Research Award in recognition of his contributions to computational imaging. He is also a Fellow of the IEEE and Eurographics, acknowledging his significant impact on the field.

Professor Heidrich's core research interests are in computational imaging and display, an emerging research area within visual computing, which combines methods from computer graphics, machine vision, imaging, inverse methods, optics and perception to develop new sensing and display technologies.

Computational imaging is the hardware-software co-design of imaging devices, which aims to optically encode information about the real world in such a way that image sensors can capture it. The resulting images represent detailed information such as scene geometry, motion of solids and liquids, multi-spectral information or high contrast (high-dynamic range), which can then be computationally decoded using inverse methods, machine learning and numerical optimization.

Heidrich and his colleagues in the Computational Imaging Group develop end-to-end learned imaging systems, increasing the complexity of the optical design space and expanding the methodology to fully automate the design of complex optical systems instead of individual components.

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.

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.

Charalambos Konstantinou is an Associate Professor of Electrical and Computer Engineering (ECE) and Affiliate Professor of Computer Science at KAUST. He is also the principal investigator of the Secure Next Generation Resilient Systems (SENTRY) Lab.

Professor Konstantinou received a Ph.D. in Electrical Engineering from New York University (NYU), U.S., and a Dipl. Ing. M.Eng. Degree in Electrical and Computer Engineering from the National Technical University of Athens (NTUA), Greece. Before joining KAUST, he was an Assistant Professor with the Center for Advanced Power Systems (CAPS) at Florida State University, U.S.

His research interests include critical infrastructure security and resilience, with a special focus on smart grid technologies, renewable energy integration and real-time simulation.

He co-chairs the IEEE Task Force on Cyber-Physical Interdependence for Power System Operation and Control and previously chaired the IEEE Task Force on Resilient and Secure Large-Scale Energy Internet Systems. He is also an associate editor of the IEEE Transactions on Industrial Informatics.

Konstantinou is a senior member of the IEEE, a member of the ACM and an ACM Distinguished Speaker (2021-2024).

Professor Konstantinou's research focuses on critical infrastructure security and resilience, with a specialization in smart grid technologies, renewable energy integration and real-time simulations. His SENTRY Lab investigates the cybersecurity and resilience of industrial control systems, critical power grid infrastructure and embedded systems.

The lab employs a "red team/blue team" approach, where researchers act as attackers ("red team") to test the defenses developed and deployed by the "blue team," who respond to the simulated intrusions.

Using this concept, SENTRY researchers design adaptive modeling methods, monitoring schemes and control algorithms to detect, prevent and mitigate the risk of cyberattacks, especially in critical grid infrastructures.

The group's research aims to create secure and resilient computing systems by employing computer security fundamentals and cyber-physical engineering applications.

David Keyes is a professor in the Applied Mathematics and Computational Sciences, Computer Science, and Mechanical Engineering programs. He served as a founding dean of the Mathematical and Computer Sciences and Engineering Division from 2009 to 2012 and as the director of the strategic initiative and ultimately the Research Center in Extreme Computing from 2013 to 2024. He is also an adjunct professor and former Fu Foundation Chair Professor of Applied Physics and Applied Mathematics at Columbia University, and a faculty affiliate of several laboratories of the U.S. Department of Energy.

Professor Keyes is Fellow of the Society for Industrial and Applied Mathematics (SIAM), the American Mathematical Society (AMS), and of the American Association for the Advancement of Science (AAAS). He is the recipient of the SIAM Prize for Distinguished Service to the Profession (2011), the Distinguished Faculty Teaching Award of Columbia University (2008), the Sidney Fernbach Award of IEEE Computer Society (2007), and the ACM Gordon Bell Prize (1999), and the Prize for Teaching Excellence in the Natural Sciences of Yale University (1991) .

Keyes graduated summa cum laude in Aerospace and Mechanical Sciences with a certificate in Engineering Physics from Princeton in 1978 and earned a doctorate in Applied Mathematics from Harvard in 1984. He was a Research Associate in Computer Science at Yale University 1984-1985, and has had decadal research appointments at the Institute for Computer Applications in Science and Engineering (ICASE), NASA-Langley Research Center, and the Institute for Scientific Computing Research (ISCR), Lawrence Livermore National Laboratory.

Keyes' research lies at the algorithmic interface between parallel computing and the numerical analysis of partial differential equations (PDEs), with a focus on scalable implicit solvers and nonlinear and linear preconditioning for large-scale applications in energy and environmental science on emerging for power-austere emerging architectures. 

Target applications demand high performance because of high resolution, high dimension, and high fidelity physical models and/or the “multi-solve” requirements of optimization, control, sensitivity analysis, inverse problems, data assimilation or uncertainty quantification. Newton-Krylov-Schwarz (NKS, 1994) and Additive Schwarz Preconditioned Inexact Newton (ASPIN, 2002) are methods he co-created and popularized. He also focuses on the discovery of data sparsity and the exploitation of hierarchy in large-scale systems involving dense covariance and kernel matrices in statistics, genomics, data science, and machine learning. 

Charters for his research are the International Exascale Software Project (IESP, 2011) and the Science-based Case for Large Scale Simulation (SCaLeS, 2001/2003) reports.

Eric Feron is an Electrical and Computer Engineering Program professor,  an affiliate of the Mechanical Engineering Program, and the Principal Investigator of the Aerospace and Transportation Systems (ATS) Research Group at KAUST.

His research focuses on the development of advanced control and optimization techniques for autonomous systems with applications in aerospace, robotics and transportation. At KAUST, he leads efforts in exploring innovative solutions for complex challenges in these fields, emphasizing safety, reliability and efficiency in autonomous systems' design and operation.

His academic journey began in Paris, where he earned a B.S. from École Polytechnique in 1989 and an M.S. from École Normale Supérieure in 1990. He completed his Ph.D. in Aerospace Engineering at Stanford University in 1994. Before joining KAUST in October 2021, he served as a faculty member at the Georgia Institute of Technology and the Massachusetts Institute of Technology’s Aeronautics and Astronautics Department.

Throughout his career, he has taught a wide range of courses, including cyber-physical systems, control systems, and flight mechanics, and is a strong advocate for quality online education resources.

Professor Feron has contributed significantly to both theoretical advancements and practical implementations in control systems, fostering collaborations across disciplines to drive progress in aerospace engineering.

With 31 years of experience in teaching and research, Professor Feron focuses on applying fundamental concepts of control systems, optimization, and computer science to modern aerospace engineering and robotics. His specific research interests include aerobatic control of uncrewed aerial vehicles, multi-agent operations, air traffic control systems and aerospace software system certification. He is also interested in geometric control systems and control theory in general.

Dr. Feron’s ATS research group has made significant technical contributions across a variety of fields, including aerospace engineering, automotive engineering, ocean engineering, biological engineering, electrical engineering, mechanical engineering and robotics, as well as human-machine interaction. These contributions are grounded in a strong foundation of mathematics, computer science and operations research.

Dr. Wittum obtained his Ph.D. (Dr. rer. nat.) in 1987 from Kiel University, Germany. He then pursued further academic qualifications at the University of Heidelberg, Germany, where he received his Habilitation in 1991 and began his first professorship in numerical analysis.

His academic career continued to advance as he served as Director of the Institute for Computer Applications at the University of Stuttgart, Germany, from 1994 to 1998. Following this, he became the Director of the Simulation in Technology Center at the University of Heidelberg, Germany, a position he held from 1998 to 2008. In 2008, he transitioned to the University of Frankfurt, where he led the Gauss Center of Scientific Computing (G-CSC).

After 25 years of serving as a professor at several leading universities in Germany, he joined KAUST, where he is currently a professor in the Applied Mathematics and Computational Science program.

Wittum’s work developing robust and scalable multi-grid methods and software systems for large-scale computing has led to numerous collaborative projects with industry partners, including ABB, Boston Consulting, Commerzbank, Daimler-Benz, the GICON Group, GRS, Porsche and more. 

His contributions to science have been recognized with several prestigious awards, including the Heinz-Maier-Leibnitz Prize, the Controlled Release Society's Award and the doIT Software Award. Professor Wittum has also authored over 200 scientific publications.

Professor Wittum’s research focuses on a general approach to modelling and simulation of problems from empirical sciences, in particular using high-performance computing (HPC).

Particular areas of focus include the development of advanced numerical methods for modelling and simulation, such as fast solvers like parallel adaptive multi-grid methods, allowing for application to complex, realistic models; the development of corresponding simulation frameworks and tools; and the efficient use of top-level supercomputers.

Wittum applies his methods and tools toward problem-solving in computational fluid dynamics, environmental research, energy research, finance, neuroscience, pharmaceutical technology and beyond.

Professor Jesper Tegnér holds a dual role as a professor at KAUST and a Strategic Professor at the Karolinska Institute in Stockholm, Sweden. He earned the rank of chaired full professor just 4.5 years after completing his M.D./Ph.D. in 1997. In 1998, he was recruited as an Assistant Professor in the Department of Computer Science and Numerical Analysis at the Royal Institute of Technology, Stockholm. During a leave of absence, Tegnér pursued postdoctoral research in Boston, U.S., supported by a Wennergren Fellowship and the Alfred P. Sloan Foundation Fellowship from 1998 to 2001.

By 2002, Tegnér had become the first chaired full professor and director of the Division of Computational Biology in Sweden. In January 2010, he took on the role of strategic professor in computational medicine at the Center for Molecular Medicine, Karolinska Institute, and Karolinska University Hospital. In 2014, he furthered his research pursuits by joining the Science for Life Laboratories in Stockholm.

Tegnér is also a Senior Editor of Progress in Preventive Medicine, an acting Section Editor for Clinical and Translational Systems Biology in Current Opinion on Systems Biology, and serves on the editorial boards of BMC Systems Biology and Neurology: Neuroinflammation & Neurodegeneration. He is a fellow of the European Society for Preventive Medicine and the founder of two BioIT companies.

Professor Tegnér's research focuses on computational medicine, systems biology, and the development of AI-driven tools for translational and preventive medicine. His interdisciplinary work integrates biological, computational, and clinical data to explore complex disease mechanisms and develop innovative therapeutic strategies. With over 350 publications, an H-index exceeding 60, and more than 20,000 citations, Tegnér is recognized as a leader in his field.

Professor Jesper Tegnér’s research is driven by two fundamental questions:

1. How can we construct reasoning or intelligent systems?
2. How can we understand living systems, specifically as a form of matter?

These two questions are deeply intertwined. Progress in constructing intelligent systems (question 1) informs the understanding of living systems (question 2), and insights gained from studying living systems guide the development of intelligent systems.

To address question 2, Tegnér’s work focuses on cellular systems as the basic building blocks of life and the brain. His team develops algorithms, theory, and data, while also conducting experiments (such as Single-Cell Genomics and Spatial Transcriptomics) to decode and model cellular networks, tissues, and organs. Their goal is to create a comprehensive field theory for non-equilibrium, dissipative, non-linear cellular systems, and ultimately, a 3D molecular map of the human brain.

In tackling question 1, Tegnér’s research targets the creation of systems capable of generating models of their environment through observation, functioning as an "artificial scientist." These systems utilize algorithmic complexity, network theory, and dynamical systems as constraints for machine learning-driven analysis, particularly in understanding living systems.

Interconnected Hypothesis

Tegnér’s work is based on the hypothesis that the mechanisms governing living systems, from molecular circuits to brain function, are deeply interconnected. He believes that a deeper understanding of cellular and brain operations is crucial for making fundamental advancements in artificial intelligence beyond mere engineering applications.

Translational Research

Tegnér’s work has significant translational applications, driven by expertise in genomics, bioinformatics, machine learning, and medicine. His projects span a wide range of biomedical systems analyses, including collaborations with clinicians worldwide on diseases such as melanoma, breast cancer, multiple sclerosis, Alzheimer’s, and others. Additional projects include:

• HLA-based banking of induced stem cells in Saudi Arabia.
• Development of large language models for Arabic speech.

Tegnér has published over 150 papers related to this translational work, reflecting the broad impact of his research across multiple fields.

Mikhail Moshkov is a professor of Applied Mathematics and Computational Science (AMCS) and an affiliated professor of Computer Science (CS) at KAUST. He is also the principal investigator of the Extensions of Dynamic Programming, Machine Learning, Discrete Optimization (TREES) research group.

Professor Moshkov holds an M.S. summa cum laude in 1977 from the University of Nizhni Novgorod, Russia. He obtained his Ph.D. in 1983 from the University of Saratov, Russia, and a Doctor of Science in 1999 from Moscow State University, Russia.

Before joining KAUST, he held professorships at the University of Nizhni Novgorod, Russia, and the University of Silesia, Poland.

Moshkov received the State Scientific Stipend in Mathematics for Outstanding Scientists from April 2000 to March 2003, awarded by the Presidium of the Russian Academy of Sciences. Additionally, he received the First Degree Research Prize, awarded by the rector of the University of Silesia, Poland, in 2006.

Professor Moshkov's research interests include: (i) The study of time complexity of algorithms in computational models such as decision trees, decision rule systems and acyclic programs with applications to combinatorial optimization, fault diagnosis, pattern recognition, machine learning, data mining, and analysis of Bayesian networks. (ii) The analysis and design of classifiers based on decision trees, reducts, decision rule systems, inhibitory rule systems, and lazy learning algorithms. (iii) Extensions of dynamic programming for sequential optimization relative to different cost functions and for study of relationships between two cost functions with applications to combinatorial optimization and data mining.

David Pugh completed his postdoctoral work at the University of Oxford in 2016, following an M.Sc. in 2009 and a Ph.D. in Economics in 2014 from the University of Edinburgh. He previously earned a B.S. in Mathematics from the College of William and Mary in 2005.

Professor Pugh leads the theme Accelerating GenAI Adoption in the KAUST Center of Excellence for Generative AI (GenAI). His work focuses on developing GenAI platforms to support innovations and applications, building capacity through training and residency programs, and promoting outreach activities to foster broader GenAI adoption, particularly among non-experts.

David has significant experience developing applications using machine learning, deep learning, and generative AI, particularly Large Language Models (LLMs).

His experienced research software engineer and data scientist who loves to teach. he just finished developing training materials to help data scientists get started managing their virtual environments with Conda and Docker. Currently developing data engineering solutions to accelerate distributed training of deep neural networks on HPC resources. He have a deep knowledge of the core data science Python stack: NumPy, SciPy, Pandas, Matplotlib, NetworkX, Jupyter, Scikit-Learn, PyTorch, TensorFlow.

His teaching interests span the across the entire AI pipeline, from data collection and pre-processing to model training, evaluation, and deployment. He is passionate about teaching students how to build, fine-tune and deploy AI models effectively and responsibly and emphasizes the importance of understanding the ethical and societal implications of AI technology.

Malek Smaoui received her M.Sc. in 2009 and Ph.D. in Computer Science in 2011 from the University of Houston (UH), U.S. She previously earned a B.E. in Electrical and Computer Engineering from École Polytechnique de Tunisie, Tunisia, in 2006.

Her teaching career began during her Ph.D. years, where she served as a teaching assistant across various Computer Science courses at the University of Houston. In parallel, she was a key contributor to the development and maintenance of the Virtual Prairie BOINC project. Smaoui joined KAUST in 2012 and has since been teaching fundamental Computer Science subjects within the Computer Science Program, primarily introducing students from other disciplines to computing at various levels of expertise.

Initially, Smaoui's research focused on nature-inspired optimization algorithms, volunteer computing and high-performance computing.

Her current primary focus is Computer Science education methods and technologies. Malek strives to enhance her students' computer science learning experiences at a graduate level, via the implementation of modern approaches and the use of recent technology innovations in the area. She mainly aims at making learning computing accessible to students from all backgrounds.

Ali is a Senior Research Staff Scientist within the CybeResil group, and is currently mentoring the group's research under the direction of Prof. Paulo Esteves-Verissimo. Ali was part of the core research team at the foundation of the former KAUST's RC3 Center and recently the Cyber Security and Resilience Community (CriSys), whose mission is to improve the state of the art and practice of Cyber-secure Cyber-resilient Cyber-physical systems. Ali is also project lead of building and operating the Cyber Security and Resilience (CSR) lab at KAUST. 

Ali has a hybrid academic and industrial research leadership experience. He was a co-founder and the head of Cybersecurity and Smart Distributed Systems research and innovation team at the industrial VORTEX CoLAB (Capgemini Group). He was in charge of the entire R&I process: strategy, scouting, ideation, conception, design, implementation, proof, validation, evaluation, publication, patenting, and pitching, and interviewing.

Prior to that, Ali worked as Assistant Researcher at INESC TEC (HASLab research unit), Portugal, where he founded with his co-authors the mainstream models for CRDTs (a.k.a., Conflict-free Replicated  Datatypes). The work has seen significant adoption in the Geo-replicated scalable available systems (among them, Facebook Apollo, PayPal, SoundCloud, TomTom, Cassandra DB, Microsoft Azure CosmosDB). In 2012-2013, he worked as Postdoc at INSA de Lyon (France), focusing on scalable anonymous communications under malicious and rational attacks, following a Game Theory Nash Equilibrium model. In 2012, he obtained his PhD degree with European Label in Computer Science from the University of Toulouse, France, working on Adaptive Byzantine/malicious/intrusion tolerant protocols. During his PhD, he visited EPFL (Switzerland) twice, hosted by Rachid Guerraoui who co-mentored his PhD. Ali was also an Invited Assistant Professor at the Department of Informatics of University of Minho and at MAP-I PhD school (Portugal). He founded, coordinated, and taught a new PhD course on "Successful Systems in Production", taught Cyber Security courses co-organized by KAUST Academy and the Saudi Nation Cybersecurity Authority (NCA), and also taught Master-level courses on Security, Blockchain, and available Geo-replicated systems topics.

Ali is and has been advising 3 PhD students and several Masters students on topics related to resilient Byzantine/intrusion tolerant available systems and cyber-secure automotive systems. Ali is currently mentoring several Research Scientists. Postdocs, interns, and students within the CybeResil group.

Foremost, Ali has a strong belief in research and innovation that serve humanity as a priority.

In a nutshell, Ali's main interest lies in both research and practice that revolve around understanding and building Cyber Secure and Resilient, scalable, available, efficient, green, smart, and distributed systems.

More recently, his focus has been on the Cybersecure Cyber-resilient Automotive industry (i.e., Autonomous Vehicles, Connected Vehicles, V2X), Cyber-physical systems (i.e., smart and connected infrastructures), small Satellite constellations, Hardware FPGA security and resilience, and Blockchain/Distributed Ledgers. Given the diversity and multidisciplinary nature of these areas, Ali is interested in any research topics that intersect and make the aforementioned topics better (in all senses). 

Throughout his research career, Ali's work spanned diverse topics on Byzantine/malicious Fault/intrusion tolerance,  Blockchains, Resilience, Cybersecurity, Security, Anonymous Communication, Cloud/Fog/Edge Computing, Automotive, and data management (Conflict-free Replicated DataTypes - CRDTs). 

Ammar El Falou received in 2009 his M.E. in Communication and Computer Engineering from the Lebanese University, Lebanon, and the M.Sc. degree in Digital Telecommunication Systems from TELECOM Paris and UPMC, Paris, France. In 2013, he received his Ph.D. in communication and information science from IMT Atlantique, Brest, France. In 2014, he was a postdoctoral at Orange Labs, Sophia Antipolis, France. From 2015 to 2020, he was an assistant professor at Lebanese University (LU), Beirut Arab University (BAU), and Lebanese International University (LIU). Before joining KAUST, he was an assistant professor at ESIEE Paris, France.

Ammar El Falou is interested in Network Security, Gaming Security, and Wireless Communications.

Dr. Hatem Ltaief is a Principal Research Scientist in the Computer Electrical and Mathematical Sciences and Engineering Division at KAUST. His research focuses on mixed-precision algorithms, low-rank matrix computations, parallel programming models, and performance optimizations for high-performance computing (HPC) systems equipped with hardware accelerators.

He has contributed to integrating numerical algorithms into major scientific libraries including NVIDIA cuBLAS and Cray LibSci. Collaborating with domain scientists across diverse fields such as ground-based astronomy, geospatial statistics, computational chemistry, bioinformatics, and geophysics, Dr. Ltaief helps their scientific applications meet the exascale computing challenges.

Dr. Ltaief has co-authored all four of KAUST Gordon Bell finalist papers since 2022. In November 2024, he received the prestigious ACM Gordon Bell Prize (shared) in climate modeling for his contributions to developing an exascale climate emulator. This groundbreaking work addresses the computational and storage demands of high-resolution Earth System Model simulations and was achieved in collaboration with a distinguished team of experts.

He earned his engineering degree from Polytech Lyon at the University of Claude Bernard Lyon I in 2003, followed by an M.Sc. in applied mathematics in 2004 and a Ph.D. in computer science from the University of Houston in 2008. Before joining KAUST, Dr. Ltaief served as a research scientist at the Innovative Computing Laboratory in Knoxville Tennessee.

Dr. Ltaief has received multiple accolades including the Best Paper Award at the ACM PASC conference in 2018 and the Gauss Award for Best Paper at the ISC Conference in 2020. He currently serves as co-Editor-in-Chief of the ACM Transactions on Mathematical Software and as an Associate Editor-in-Chief of the Elsevier Parallel Computing Journal.

Dr. Hatem Ltaief's research focuses on mixed-precision algorithms, parallel numerical algorithms, parallel programming models, and performance optimizations for manycore architectures and high-performance computing.

I got my PhD in 2006 from Grenoble Inst. of Tech., Grenoble, France. After 2 years as a postdoc in Munich, Germany, I was recruited as a permanent researcher by the CNRS in 2008. I spent 4 years in the GREYC, Caen, and 7 years in GIPSA-Lab, Grenoble. From 2016 to 2019, I was a member of the French National Committee for Scientific Research (CoNRS, Section 7). Since Nov. 2019, I am on leave from the CNRS and a senior researcher at KAUST.

Optimization: deterministic and stochastic algorithms, convex relaxations. Applications to machine learning, signal and image processing

Peter started his academic life at Vienna University of Technology (TU Wien). He obtained his bachelor degree in Media Computer Science in 2003. After that, he worked as a teacher assistant for one year. In 2005, he received his master degree in Computer Graphics and Digital Image Processing. During his preparation for the master degree, he worked part-time as a Computer Science teacher at a School for Social Services.

He was a Research Assistant from 2005 to 2009 at TU Wien. In that period, he was also preparing doing his PhD in Computer Science. He received his doctoral degree in 2009. Before joining KAUST, Peter was a Postdoctoral Fellow at the Vienna University of Technology until 2011.

Peter is interested in Scientific Visualization, particularly in advanced methods of fluid flow visualization, Computer Graphics in general, Augmented and Virtual Reality, as well as large language models (LLMs) and their applications in interactive systems.