Profiles

Omar Knio received his Ph.D. in mechanical engineering in 1990 from the Massachusetts Institute of Technology (MIT) in the United States. He held a postdoctoral associate position at MIT before joining the mechanical engineering faculty at Johns Hopkins University in 1991. In 2011, he joined the Department of Mechanical Engineering and Materials Science at Duke University, where he also served as associate director of the Center for Material Genomics. In 2012, he was named the Edmund T. Pratt, Jr. Professor of Mechanical Engineering and Materials Science at Duke.

In 2013, Knio joined the Applied Mathematics and Computational Sciences (AMCS) Program at KAUST, where he also served as deputy director of the SRI Center for Uncertainty Quantification in Computational Science and Engineering and as the interim dean of the Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division. In 2024, he was appointed associate vice president of National Partnerships, Engagement and Academic Liaison, at the KAUST National Transformation Institute.

He is a founding associate editor of the SIAM/ASA Journal on Uncertainty Quantification and currently serves on the editorial boards of the International Journal for Uncertainty Quantification and Theoretical and Computational Fluid Dynamics.

Knio has received several awards, including the Associated Western Universities Faculty Fellowship Award in 1996, the Friedrich Wilhelm Bessel Award in 2003, the R&D 100 Award in 2005, the Distinguished Alumnus Award from the American University of Beirut in 2005, and the Abdul-Hameed Shoman Award for Arab Researchers in 2019.

Professor Knio’s research interests include uncertainty quantification, Bayesian inference, combustion, oceanic and atmospheric flows, physical acoustics, energetic materials, microfluidic devices, renewable energy systems, high-performance computing, optimization under uncertainty, and data-enabled predictive science.

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.

Dr. Moraga graduated in mathematics from the University of Valencia, Spain, with an Erasmus year abroad at the Johannes Gutenberg University of Mainz, Germany. Following graduation, she worked for a technological company, developing algorithms for optimal investment strategies. After that, she enrolled in the Ph.D. program at the University of Valencia and worked at the office for regional statistics and the national cancer registry. During her doctoral studies, she was awarded the prestigious "la Caixa" Fellowship for studying for her Master’s degree in Biostatistics at Harvard University, U.S.; this complemented her mathematical background with a solid knowledge of biostatistics and epidemiology. She also received an Ibercaja Research Fellowship to carry out a research project at the Harvard Medical School, a stipend from the Google Summer of Code Program to write code for the R project, and completed a traineeship at the European Center for Disease Prevention and Control (ECDC).

After obtaining her Ph.D. with Extraordinary Award, she was appointed to academic statistics positions at Lancaster University, U.K., Harvard School of Public Health, U.S., the London School of Hygiene & Tropical Medicine, U.K., Queensland University of Technology, Australia, and the University of Bath, U.K. During this time, she worked in statistical research, focusing on spatial epidemiology and modeling, especially concerning spatial and spatio-temporal variation in infectious diseases and cancer. She developed modeling architectures to understand the spatial and spatio-temporal patterns and identify targets for intervention of diseases, such as malaria in Africa, leptospirosis in Brazil and cancer in Australia, and several R packages for Bayesian risk modeling, detection of clusters and risk assessment of the travel-related spread of disease.

In 2020, she joined KAUST as an Assistant Professor of Statistics and the principal investigator of the Geospatial Statistics and Health Surveillance (GeoHealth) research group. In the GeoHealth group, she develops frontier geospatial methods and computational tools for geospatial data analysis and health surveillance. She also contributes to a wide range of collaborative projects to solve challenging problems in public health and make a positive impact on the world.

Dr. Moraga is the 2023 winner of the prestigious Letten Prize. Awarded by the Letten Foundation and the Young Academy of Norway, the prize recognizes young researchers’ contributions to health, development, environment, and equality across all aspects of human life. She received the Letten Prize for her pioneering research in disease surveillance and her significant contributions to the development of sustainable solutions for health and the environment globally.

Dr. Moraga has worked in statistical research for over a decade, focusing strongly on spatial epidemiology and modeling. She develops innovative statistical methods and computational tools for geospatial data analysis and health surveillance, including methods to understand geographic and temporal patterns of diseases, assess their relationship with potential risk factors, identify clusters, measure inequalities and quickly detect outbreaks.

Dr. Moraga is a fervent advocate for open science and reproducible research. She has created educational materials that impact learning on a large scale, including her books "Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny" (https://www.paulamoraga.com/book-geospatial/) and "Spatial Statistics for Data Science: Theory and Practice with R" (https://www.paulamoraga.com/book-spatial/). Her training courses support researchers as they develop sustainable solutions to local issues, and her books have been cited in works that address multiple diseases and health conditions such as COVID-19, neglected tropical diseases, cancer, anemia, malnutrition, child maltreatment, and mental issues.

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.

Professor Markowich earned an M.S. and a Ph.D. in Habilitation for Applied and Numerical Mathematics at the Vienna University of Technology (TU-Wien), Austria. He became a Full Professor at the Technical University of Berlin (TUB) in 1989. From 1999 until 2007, he worked at the University of Vienna, Austria, as a Professor of Applied Analysis; from 2007-2011, he worked at the University of Cambridge, U.K., as a Professor of Applied Mathematics. Since 2011, he has been a Distinguished Professor at KAUST.

The Austrian-Italian researcher is a prolific researcher and author, with nearly 14,000 citations and more than 200 scientific papers in top international journals. He has authored a series of books presenting topics of science and engineering found in nature or everyday life. In the books, physical variables such as mass, velocity and energy are analyzed using partial differential equations, along with their spatial and temporal variations.

Professor Markowich has been honored with numerous awards and recognitions throughout his career; these include the Wittgenstein Prize from the Austrian Science Fund, The Royal Society Wolfson Research Merit Award and the Humboldt Research Award.

In 2015, and again in 2018, he held a J.T. Oden Faculty Fellowship at the Oden Institute for Computational Engineering and Sciences (University of Texas), the Von Neumann Visiting Professorship at the Technical University Munich, Germany, in 2013; the Excellence Chair at Jiaotong University, Shanghai, China, in 2012; and the Excellence Chair of the Foundation Sciences Mathématiques de Paris, France, in 2011.

Markowich is a Fellow of the European Academy of Sciences, the Institute of Physics, the American Mathematical Society and the Institute of Mathematics and its Applications. He is also a member of the European Academy of Sciences and Arts, Academia Europaea and a Foreign Member of the Austrian Academy of Sciences.

Dr. Markowich’s research uses differential equations in physics, artificial intelligence, biology, and engineering. Specifically, he is interested in deepening the understanding of the mathematical and numerical analysis of partial differential equations (PDEs) and their applications.

In particular, he is interested in:

• classical and quantum mechanical kinetic theory
• analytical and numerical problems occurring in highly oscillatory PDEs (like semiclassical asymptotics)
• Wigner transforms
• nonlinear PDEs describing dispersive and, resp., diffusive phenomena
• singular perturbations and longtime asymptotics
• generalized Sobolev inequalities
• inverse problems in solid-state physics
• image processing using PDEs.

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.

Raphaël Huser is an Associate Professor of Statistics and the principal investigator of the Extreme Statistics (XSTAT) research group. He is also affiliated with the Applied Mathematics and Computational Science (AMCS) Program.

Professor Huser received his Ph.D. in Statistics in 2013 from the Swiss Federal Institute of Technology, Switzerland, under the supervision of Professor Anthony C. Davison. He also holds a B.S. in Mathematics and an M.S. in Applied Mathematics from École polytechnique fédérale de Lausanne (EPFL), Switzerland.

After completing his Ph.D., Huser joined KAUST as a postdoctoral research fellow in January 2014. He was appointed Assistant Professor in March 2015 and promoted to Associate Professor of Statistics in 2022.

Raphaël Huser’s research primarily focuses on statistics of extreme events and risk assessment, including developing specialized statistical models with appealing statistical properties. Additionally, he studies efficient machine learning methods designed for massive datasets from complex spatio-temporal processes.

Huser’s novel methodological contributions are motivated and inspired by a wide variety of real data applications, which include the modeling of natural hazards in climate and earth sciences (e.g., heavy rainfall, heat waves, extreme sea surface temperatures, strong wind gusts, devastating landslides), the assessment of financial risk (e.g., turbulence in stock markets or cryptomarkets), and the characterization of brain signals during extreme stimuli (e.g., epileptic seizures).

Beyond creating new models with interesting statistical properties, one crucial aspect is fitting these complex models to big data. A critical area of Huser's current research is developing general-purpose, likelihood-free, fast and statistically efficient neural Bayes estimators. Being deeply anchored in statistical decision theory and relying on advanced deep-learning techniques, which makes them attractive both from a theoretical and a computational perspective, these estimators truly provide a paradigm shift challenging traditional statistical inference techniques for complex models with intractable likelihoods.

Huser, with collaborators, is contributing extensively to the early development of such estimators and their application to spatial (e.g., extreme) and multivariate models.

Professor Tempone received his Ph.D. in Numerical Analysis ('02) from the Royal Institute of Technology, Sweden. The next phase of his career took him to the United States, where he completed his postdoctoral studies at the University of Texas' Institute for Computational and Engineering Sciences (ICES), before joining Florida State University as an Assistant Professor of Mathematics.

Tempone joined KAUST in 2009 as a founding faculty, with the rank of Associate Professor of Applied Mathematics before becoming a Full Professor of Applied Mathematics in 2015. He is also the principal investigator of the Stochastics Numerics Research Group at KAUST.

A variety of fields, such as computational mechanics, quantitative finance, biological and chemical modelling and wireless communications, are driving his research. More specifically, his research contributions include a posteriori error approximation and related adaptive algorithms for numerical solutions to deterministic and stochastic differential equations. His honors include the German Alexander von Humboldt professorship (2018-2025) and the first Dahlquist Fellowship in Sweden (2007-2008). He was elected Program Director of the SIAM Uncertainty Quantification Activity Group (2013-2014).

Professor Raul Tempone's expertise and research interests lie at the intersection of applied mathematics, computational science, and stochastic analysis, with a strong focus on developing and analyzing numerical methods for stochastic and deterministic problems. His work emphasizes adaptive algorithms, Bayesian inverse problems, scientific machine learning, stochastic optimization, and uncertainty quantification, aiming to push the boundaries of computational efficiency and accuracy in simulations.

At the helm of the Stochastic Numerics Research Group at KAUST, Tempone is particularly interested in applications spanning computational mechanics, quantitative finance, biological and chemical modeling, and wireless communications. His research group is dedicated to tackling a posteriori error approximation, data assimilation, hierarchical and sparse approximation, optimal control, optimal experimental design, and the rigorous analysis of numerical methods.

Professor Tempone's approach is not only theoretical but also highly applicable, seeking to address real-world problems in various domains by leveraging mathematical and computational techniques. His work is instrumental for those interested in the practical application of mathematics to solve complex, real-world issues, making his research group an ideal place for potential collaborators, postgraduate students, postdocs, and research scientists looking for cutting-edge projects at the nexus of uncertainty quantification and computational science.

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.

Rolf Krause is a full professor in the Applied Mathematics and Computational Sciences Program at KAUST, with a career spanning academia, research and leadership. Before joining KAUST, he was a full professor at Università della Svizzera italiana in Lugano, Switzerland, where he directed the Institute of Computational Science from 2009 to 2020 and has served as co-director of the Center for Computational Medicine in Cardiology since 2014.

Beyond his research roles, Professor Krause has held notable leadership positions including a director of the interdisciplinary Euler Institute in 2021 and was the founding dean of the Faculty of Mathematics and Informatics at UniDistance Switzerland in 2022.

His commitment to academic service includes roles such as chairman of the examination board for mathematics studies and chairman of the board of finance for tuition fees at the University of Bonn from 2007 to 2009, as well as membership in the academic senate at USI from 2017 to 2021.

His work has earned numerous awards, including the Taylor & Francis Prize for Innovative Contribution to Theoretical Biomechanics/Biomedical Engineering and the MATH+ Distinguished Visiting Scholar recognition from the MATH+ Center in Berlin.

Professor Krause holds a Doctor rerum naturalium in Mathematics with distinction ("summa cum laude") from The Free University of Berlin, awarded in 2001. He also earned a Diploma in Mathematics with a minor in Economics from the same institution in 1996.

Professor Krause's research focuses on numerical simulation, machine learning, optimization, and data-driven approaches. A major focus of his research is the design and analysis of efficient and reliable algorithms that can be used to solve complex problems in scientific computing and machine learning.

Krause and his colleagues use mathematical understanding and computer science expertise to advance sustainable progress in many areas, from medicine to geology. They provide scientific software capable of solving complex, large-scale problems that can run on modern supercomputers such as KAUST’s Shaheen III.

Areas of expertise and focus

• Contact problems in mechanics
• Scientific software
• Multilevel and domain decomposition methods
• Optimization
• Iterative solution of large-scale systems
• Parallel computing
• High-performance computing (HPC)
• Coupled problems
• Finite elements
• Non-linear solution methods
• Neural networks
• Physics-informed neural networks
• Cardiac simulation
• Biomechanics
• Computational geoscience

Application areas

• Medicine
• Computational mechanics
• Contact problems
• Fluid-structure interaction
• Cardiac simulation
• Biomechanics
• Geology
• Complex and coupled multiphysics

Shehab Ahmed is a professor of Electrical and Computer Engineering (ECE) at KAUST, a position he has held since August 2018. He is affiliated with the Physical Science and Engineering (PSE) Division. Professor Ahmed first joined KAUST in 2010 as a visiting assistant professor in the PSE Division, and has since become a key contributor to the University's research landscape.

Beyond his work at KAUST, Shehab Ahmed held various academic roles at Texas A&M University at Qatar (TAMUQ). His tenure included serving as Associate Professor from 2013 to 2018, Assistant Professor from 2007 to 2013, and earlier as a Research Assistant from 1999 to 2002. In parallel with his academic career, Ahmed spent seven years as an electrical engineer at the Schlumberger Integrated Productivity and Conveyance Center in the U.S., where he applied his expertise in real-world engineering challenges.

Ahmed is the author of more than two hundred highly cited publications. He is also the recipient of prominent awards, such as the TEES Engineering Genesis Award, the ARC Best Research Project (2016) and the TAMUQ Faculty Research Excellence Award.

He received his Ph.D. and M.Sc. in Electrical Engineering from Texas A&M University, U.S., in 2007 and 2000, respectively. Ahmed obtained his B.Sc. in Electrical Engineering from Alexandria University, Egypt, in 1999.

Professor Ahmed’s research interests span the broad areas of power conversion and mechatronic systems. He specializes in distributed/renewable power generation, utility power conversion, microgrid energy management/storage, hybrid/electric vehicle drivetrains and high-voltage DC systems.

Among Ahmed’s goals are enhancing drilling operations, improving wellbore integrity assessment and supporting successful wireline conveyance.

Professor Park received his Ph.D. in Electrical Engineering in 2015 from the University of Maryland, U.S. Following his Ph.D., he held postdoctoral researcher positions at the National Geographic Society in 2016 and at the Massachusetts Institute of Technology (MIT), U.S., from 2016 to 2019.

He joined KAUST in January 2021 as the principal investigator of the Distributed Robotics and Autonomy (DGA) Group. Prior to joining KAUST, he served as an associate research scholar at Princeton University, U.S., where he contributed to cross-departmental robotics projects.

Professor Park’s past research includes developing animal-borne sensor networks to study wild animal groups in their natural environments. He also created a fleet of urban autonomous surface vessels designed for transporting people and providing delivery and trash removal services through urban canal networks. In 2019, his innovative work was highlighted by MIT News.

Professor Park’s research focuses on the design and control of multi-robot systems. He strives to advance robotics science and engineering and seeks innovative ways to solve societal challenges using robotics technology. He pursues new and creative approaches to synergizing the individual robots’ core capabilities and strengthening the autonomy of robotic groups to solve large-scale problems.

His DGA Group investigates innovative concepts to address fundamental research questions in multi-agent, robotics and control systems. Their central focus is conceiving novel models and computational methods for multi-agent coordination and developing and deploying robotic/control systems for monitoring real-world environments such as the Red Sea.

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.

Tareq Al-Naffouri is a professor in the Electrical and Computer Engineering (ECE) Program at King Abdullah University of Science and Technology (KAUST).

Al-Naffouri earned a B.S. (Hons.) in Mathematics and Electrical Engineering from King Fahd University of Petroleum and Minerals, Saudi Arabia, 

During the summers of 2005 and 2006, Al-Naffouri was a visiting scholar at the California Institute of Technology, U.S. He was a Fulbright Scholar at the University of Southern California, U.S., in 2008.

An IEEE Senior Member, he has produced over 370 publications in journals and conference proceedings and 24 issued/pending patents. Al-Naffouri received the IEEE Education Society Chapter Achievement Award (2008), the Almarai Award for Innovative Research in Communication (2009) and the Abdul Hameed Shoman Prize for Innovative Research in IoT (2022).

Inference and Learning and their applications to Wireless Communications, Localization, Smart Cities, and Smart Health

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.

Professor Xiaohang Li has extensive research experience in III-nitride and III-oxide (ultra)wide bandgap semiconductors. Prior to KAUST, Li received his Bachelor degree in Applied Physics from Huazhong University of Science and Technology, China, his Master's degree in Electrical Engineering from Lehigh University, U.S., and his Ph.D. degree in Electrical Engineering from Georgia Institute of Technology, U.S.

Since joining KAUST, Li has advised more than 100 students, and led his Advanced Semiconductor Laboratory (ASL) in making many important and pioneering contributions to semiconductor research. Prof Li and his ASL team focus on the fundamental and applied research of ultrawide and wide-bandgap semiconductor materials, devices, physics and hardware. The ASL team aims to leverage these technologies to revolutionize the energy, communications, and health industries crucial for the sustainability of human society.

Professor Li has extensive research experience in III-nitride and III-oxide (ultra)wide bandgap semiconductors. He focuses his interdisciplinary research activities on investigating the growth, simulation, fabrication and characterization of III-nitride structures for next-generation devices. Devices of particular interest include LEDs, lasers, transistors and next-generation CMOS ICs.

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.

Dr. Yating Wan is an Assistant Professor at KAUST, specializing in Silicon Photonics with a focus on integrating on-chip light sources that can be applied to data communication, optical computing, OPA based lidar, and quantum information processing. She earned her Ph.D. in 2017 from HKUST (supervised by Prof. Kei May Lau). From 2016 to 2022, she worked at UCSB (supervised by Prof. John Bowers), where she led Intel’s project on heterogeneously integrated quantum dot lasers, making significant contributions to Si CMOS-compatible light sources. 

Dr. Wan has authored over 100 peer-reviewed publications, including 29 first-author journal papers (10 as covers), 7 corresponding-author journal papers, and >20 invited talks in international conferences. With over 3,500 citations and an h-index of 33, her research has received prestigious awards, including HKUST PhD Research Excellence Award (2017), PIERS Young Scientist Award (2018), CLEO Tingye Li Innovation Prize (2021), Rising Stars of Light by Light: Science & Applications (2022), MIT Technology Review's "35 Innovators Under 35 for China" (2023), Optica Ambassador (2024), and Sony Women in Technology Award with Nature (2025). 

Outside of her immediate research spectrum, Dr. Wan has been a proactive contributor to the broader academic community. She is the Manager & Column Editor for the LSA Editorial Office in Thuwal, Associate editor in Applied Optics, IEEE JQE, guest associate editor in IEEE JSTQE, TPC of IPC and CLEO, and Committee Member of the IEEE Photonics Society (IPS) Conference Council. She has also been a referee for a myriad of prestigious journals, spanning IEEE, OSA, and the Nature Publishing Group more than 100 times.

An expert in silicon photonics, Dr. Wan specializes in on-chip light source integration. Her research extends to data communication, optical computing and quantum information processing.

She is renowned for integrating long-wavelength quantum dot devices on silicon.

Professor Ying Sun received her Ph.D. in Statistics in 2011 from Texas A&M University, U.S. Following her Ph.D., she joined the research network for Statistical Methods for Atmospheric and Oceanic Sciences (STATMOS) as a postdoctoral researcher, working at both the University of Chicago (UC), U.S., and the Statistical and Applied Mathematical Sciences Institute (SAMSI). She then served as an Assistant Professor of Statistics at Ohio State University, U.S., before joining KAUST in 2014 as an Assistant Professor.

Professor Sun has received numerous awards for her research, including the Section on Statistics and the Environment (ENVR) Early Investigator Award from the American Statistical Association (ASA) in 2017 for her significant contributions to environmental statistics. In 2016, she was honored with the Abdel El-Shaarawi Young Researcher (AEYR) Award from the International Environmetrics Society (TIES) for her outstanding work in spatio-temporal statistics, functional data analysis, and visualization, as well as her service to the profession.

Professor Sun’s research centers on developing statistical models and methods for complex data to address important environmental problems.

Her scientific research has contributed greatly to the understanding of environmental statistics. She is involved in the development of every aspect of spatio-temporal and functional data analysis—from developing informative graphical tools for functional data to building computationally efficient, yet physically realistic models for natural spatio-temporal processes.

Sun also works on broader engineering problems that require reliable statistical process monitoring and quality control.

Ying Wu is an Associate Professor of Applied Mathematics and Computational Science (AMCS) and the principal investigator of the Waves in Complex Media Research Group.

Professor Wu obtained her Ph.D. in Physics in 2008 from the Hong Kong University of Science and Technology (HKUST), which was followed by a two-year postdoctoral fellowship. She received her B.S. in Physics from Nanjing University, China, in 2002.

Wu is a dedicated physicist who studies electromagnetic, acoustic and elastic waves. Her work has advanced theoretical and design knowledge of metamaterials, photonic and phononic crystals and waves in random media.

Among Professor Wu’s research interests are computational physics with a focus on wave propagation in heterogeneous media, electromagnetic, acoustic and elastic metamaterials, effective medium theory, transport theory and time-reversal imaging. Furthermore, she implements fast algorithms for solving large-scale, classical wave propagation problems.

Derya Baran is an associate professor of Material Science and Engineering in the Organic/Hybrid Materials for Energy Applications (OMEGA) Lab in the Physical Science and Engineering (PSE) Division at King Abdullah University of Science and Technology (KAUST). She is also affiliated with the Electrical and Computer Engineering (ECE) Program in the Computer, Electrical, and Mathematical Sciences and Engineering (CEMSE) Division.

Baran’s academic journey includes roles such as Research Associate at Jülich Forschungszentrum, Germany, in 2016, and Postdoctoral Fellow at the Center for Plastic Electronics, Imperial College London, UK, in 2015. She earned her Ph.D. in Material Science and Engineering from Friedrich-Alexander University Erlangen-Nurnberg, Germany, in 2014, and holds an M.Sc. in Chemistry from Middle East Technical University, Turkey, in 2010, and a B.Sc. in Chemistry from the same institution in 2008.

Professor Baran aims to expand the applications of solution-processable organic/hybrid semiconductors and to explore their limits in organic/hybrid thermoelectric devices and bio-electronics in the future.

Professor Baran's research interests lie in the area of solution-processable organic/hybrid soft materials for electronic devices. Such soft semiconductor materials possess a viable platform for printed, large area, stretchable and wearable electronics that can be used as solar cells, smart windows, OFETs, thermoelectrics, sensors and bio-electronics.

​Professor Baran is particularly interested in interface engineering for organic/hybrid solar cells, transparent solar cells for building integrated photovoltaics and stability/degradation studies for long lifetime organic solar cells. She has led projects on i) conjugated polymers for electrochromic devices; ii) non-fullerene acceptors for organic solar cells; iii) multi-component and multi-layered solar cell devices; iv) understanding the correlation between recombination and nano-morphology in solution-processed solar cells.

Professor Husam Alshareef is the Chair of the KAUST Center of Excellence for Renewable Energy and Storage Technologies (CREST) and the Principal Investigator of the KAUST Functional Nanomaterials & Devices Laboratory, with research interests in the development of nanoscale materials for energy applications. Alshareef’s work focuses on synthesizing advanced nanomaterials and translating them into practical energy solutions, contributing significantly to innovations in renewable energy and storage technologies.

Husam is the author of 600 journal publications and holds 80 issued patents. With over 62,000 citations and an h-index of 134 (Google Scholar), he was recognized for the fifth consecutive year by the Web of Science and Clarivate Analytics as a highly cited researcher in material science, placing him in the top 1% of worldwide researchers in terms of research output.

Professor Alshareef is a Fellow of several prestigious organizations, including the American Physical Society (APS), the Institute of Electrical and Electronics Engineers (IEEE, 2024), the U.S. National Academy of Inventors (2022), the UK Institute of Physics (IoP, 2022), the Royal Society of Chemistry (RSC), and the Institute of Materials, Minerals, and Mining (2021). These accolades reflect Husam’s extensive contributions to energy storage and materials science, positioning him as a leader in the field.

Professor Alshareef's research focuses on developing nanomaterials for energy storage and electronic applications. His research efforts have recently centered on creating new battery chemistries for harsh environments, and batteries with higher energy density, lower cost, and better safety.  

Ibrahim Hoteit is a Professor of Earth Science and Engineering at King Abdullah University of Science and Technology (KAUST). He leads the Climate Change Center, a national initiative supported by the Saudi Ministry of Environment, and directs the Aramco Marine Environment Center at KAUST. Since joining KAUST in 2009, Professor Hoteit has developed extensive expertise in climate and environmental modeling, data assimilation, and uncertainty quantification for large-scale geophysical applications.

Professor Hoteit's research focuses on creating integrated data-driven modeling systems to analyze and predict atmospheric and oceanic circulation and climate patterns across the Arabian Peninsula, with a specific emphasis on the Red Sea and Arabian Gulf. He is dedicated to understanding the impacts of these climate dynamics on regional ecosystems, offering critical insights that support sustainable environmental management and inform policy development.

Professor Hoteit’s research centers on integrating dynamical models with observational data to simulate, understand, and predict geophysical fluid systems. He specializes in developing and implementing oceanic and atmospheric models, alongside data assimilation, inversion, and uncertainty quantification techniques tailored for large-scale geophysical applications.

Currently, his work emphasizes the creation of integrated data-driven modeling systems to study the circulation and climate of the Arabian Peninsula, with a specific focus on the Red Sea and Arabian Gulf and their effects on ecosystem productivity. His team further leverages advanced artificial intelligence (AI) techniques to enhance forecasting accuracy, improve model parameterizations, and address critical applications in marine and land ecosystems, as well as renewable energy.

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.

Osman M. Bakr is a professor in the Material Science and Engineering program at KAUST, where he has been a faculty member since 2010. He previously served as a Postdoctoral Fellow at the Laboratory for Nanoscale Optics at Harvard University and held research assistant positions in the Supramolecular Nanomaterials Group and Advanced Inorganic Materials Group at MIT.

He holds a Ph.D. and M.Sc. in Applied Physics from Harvard University and a B.Sc. in Materials Science and Engineering from MIT. Throughout his academic journey, Bakr has made significant contributions to the field of nanomaterials, and in 2008, he was awarded the King Abdullah Scholar Award at KAUST.

Professor Bakr's research interests are concerned with the physics and chemistry of hybrid materials. His group studies the synthesis and assembly of organic–inorganic hybrid materials and nanomaterials of novel optical and electronic properties. The purpose of these studies is to fabricate advanced material building blocks for solar cells and optoelectronic devices.

Professor Schmid's research centers on applied mathematics, with a strong emphasis on fluid dynamics and flow control. His work involves creating and applying computational models (using tools like Matlab, Simulink, and Signal Processing) to quantitatively describe and analyze physical systems.

Professor Sahika Inal is an Associate Professor of Bioengineering at KAUST. Her research lies at the intersection of organic electronics and biology, focusing on developing bioelectronic materials and devices that interface with living systems.

Her research aims to create tools that can translate biological signals into electronic signals, facilitating real-time monitoring and intervention for health applications, including tissue regeneration, diagnostics and drug delivery.

Prior to joining KAUST, Sahika Inal was a postdoctoral fellow in the Department of Bioelectronics at the Center of Microelectronics of Provence, École Nationale Supérieure des Mines de Saint-Étienne, Gardanne, France. She earned her B.Sc. in Textile Engineering from Istanbul Technical University in 2007 and her M.Sc. in Polymer Science in 2009 from a joint program involving TU, HU, FU, and the University of Potsdam in Berlin, Germany. In 2013, she completed her Ph.D. in Experimental Physics from the University of Potsdam, where her doctoral research focused on developing phase transition polymer/conjugated polyelectrolyte-based optical sensors for autonomous pathogen detection. Her M.Sc. work explored optical processes in organic solar cells using small molecule acceptors.

She hold nine patents and has delivered over 50 invited and keynote presentations at international conferences and universities across various countries. She is recognized as a Fellow of the Royal Society of Chemistry and has received several awards, including ACS PMSE Young Investigator Award 2022, Beilby Medal and Prize 2022, and the Journal of Materials Chemistry Lectureship 2022. She has authored over 130 publications, and her work has been cited more than 13000 times with a 2024 h-index of 61.

Her innovative contributions help advance the field of bioelectronics and open new possibilities for understanding and interacting with biological systems.

Sahika’s expertise lies in polymer science and bioelectronic devices. She specializes in photophysics of conjugated polymers, characterization of polymer thin films, behavior of polymer films in aqueous environment, and the design of biosensors and actuators comprising conjugated polymers. She currently investigates ion/electron conduction in organic electronic materials and designs bioelectronic devices that can record/stimulate biological signals.  

Combining in-situ techniques to monitor ion and electron motion in films, fibers, and porous scaffolds of organic materials, her team aims to find the best performing materials, formulations, processing conditions and form factors for applications in electrolytes. 

These optimized materials are then applied to build specific devices (transistors, fuel cells, electrodes, electrochemical actuators or drug delivery devices) that can sense or stimulate biological signals.

Professor Shuyu Sun earned his Ph.D. in Computational and Applied Mathematics from The University of Texas at Austin in 2003 and holds a second Ph.D. in Chemical Engineering from Tianjin University, China, completed in 1997. Before joining KAUST in 2009, he was an Assistant Professor at Clemson University and a Research Associate at the University of Texas at Austin’s Center for Subsurface Modeling.

Currently leading the Computational Transport Phenomena Laboratory (CTPL) at KAUST, Professor Sun has made significant contributions to the fields of numerical analysis and computational thermodynamics, particularly in the context of reservoir simulations and fluid dynamics. His work spans both academic and industrial applications, providing critical insights into subsurface energy resources.

Throughout his career, Professor Sun has authored and co-authored over 400 publications, contributing significantly to the fields of numerical analysis, computational thermodynamics, and reservoir simulations.

Professor Sun’s research covers a broad spectrum of topics, including the development of finite element methods (especially adaptive discontinuous Galerkin methods) for solving flow and reactive transport problems in porous media. His work also focuses on computational thermodynamics and numerical simulations of oil reservoirs. These techniques are essential for improving the accuracy of simulations related to subsurface fluid dynamics and resource extraction, with applications extending to renewable energy, climate science, and environmental sustainability.

Dr. Salem holds a Ph.D. in Electrical Engineering from Stanford University, U.S. Prior to joining KAUST in 2010, he was an Assistant Professor of Electrical Engineering at Alexandria University, Egypt. He has also jointly held an assistant professorship at the Wireless Intelligent Networks Center (WINC) at Nile University, Egypt, since 2008.

Salem has extensive experience teaching at graduate and undergraduate levels. He has taught courses in digital communications, wireless communications and systems, digital signal processing, signal and systems, detection and estimation, probability and stochastic processes, engineering mathematics, signal processing for radar systems, remote sensing, and automatic control.

In 2017, he received the inaugural KAUST Distinguished Teaching Award. His research has yielded over eighty scientific publications in various peer-reviewed journals and conferences.

Dr. Salem’s research interests include energy harvesting, dynamic spectrum access, cognitive radio networks, cooperative communications, orthogonal frequency-division multiplexing for optical communications, distributed and sequential detection, cooperative relay-based multi-hop communications, physical layer-based secrecy, remote sensing and synthetic aperture radar.

Alexandra Aguiar Gomes, a dedicated university educator since 1996, holds a Ph.D. in Aerospace Engineering, an M.Sc. in Mechanical Engineering, and a B.Sc. in Physics Engineering from Instituto Superior Técnico, Universidade Técnica de Lisboa, Portugal. Her doctoral work focused on the multidisciplinary and topology optimization of morphing aircraft wings.

Currently an Instructional Professor at KAUST's CEMSE Division, Alexandra is known for her focus on making mathematics accessible and engaging for students. 

Alexandra's area of expertise is optimization. Currently, her research interests include the mathematics of decision-making for sustainable development and the mathematics of generative artificial intelligence. 

Throughout her career, she has been recognized with multiple teaching awards, in particular, the 2020 KAUST Distinguished Teaching Award, highlighting her commitment to academic excellence and innovative teaching methods.

Alexandra's area of expertise is optimization. Currently, she has two areas of interest: the mathematics of decision-making for sustainable development and the mathematics of generative artificial intelligence,

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.

Prior to joining KAUST in 2019, Ortega Sánchez spent sixteen years working at the Mathematics Research Center (CIMAT) in Guanajuato, Mexico. He completed his postgraduate and graduate studies in London, where he studied mathematics at King’s College London before obtaining his Ph.D. in Probability Theory at Imperial College London. After his time in the U.K., Ortega Sánchez returned to his native Venezuela, where he worked for over 20 years at the Universidad Central de Venezuela and the Venezuelan Scientific Research Institute..

He has taught courses on stochastic models, time series, measure theory, advanced probability, extreme value theory, statistical consulting, functional data analysis, applied statistics, time series, and design of experiments. His career has seen him teach courses at several institutions worldwide, including the University of Paris-Sud, France, and the University of Valladolid, Spain.

Ortega’s primary role at KAUST is teaching statistics and providing additional mathematics support.

Throughout his career, Ortega’s research has focused on stochastic processes, specifically Gaussian processes and time series, with applications in oceanography and biostatistics. More recently, his work has focused on functional data analysis.

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.

Dr. Sultan Albarakati is the Director of KAUST Academy and a key leader in STEM education in Saudi Arabia. In this role, he leads initiatives to provide world-class training and educational programs for Saudi students and professionals centered on upskilling the national workforce, particularly in artificial intelligence, machine learning and data science.

His work aims to support the Kingdom's rapidly transforming economy through partnerships with universities and industry while fostering talent development, contributing to National Talent Development in line with Vision 2030.

Dr. Albarakati earned his Ph.D. in 2020 and his M.S. in 2014, both in Applied Mathematics from KAUST, following a B.S. in Mathematics from Umm Al-Qura University in 2004. 

Before joining KAUST, he played a significant role in mentoring Saudi Arabia's Math Olympiad teams, leading them to notable international success.

Talal Al-Attar has taught electrical engineering at institutions such as Kuwait University, Kuwait; Stanford University, U.S.; Santa Clara University, U.S.; and KAUST, Saudi Arabia, for over 15 years.

Professor Al-Attar received his B.S. and M.S. from Kuwait University and his Ph.D. from Stanford University. His doctoral work focused on impact ionization avalanche transit-time (IMPATT) modeling at the millimeter-wave range, on-chip integration of microstrip patch antennas and transmission lines in standard complementary metal-oxide-semiconductor (CMOS) technology.

Al-Attar’s career began as a senior design and device engineer at Volterra Semiconductor, U.S., where he worked from 2004 to 2007. He also worked as a senior consultant at Sabio Labs in 2007 before joining Magma Design Automation in 2008. Al-Attar consulted several companies between 2007 and 2014, including Ensphere Solutions, AWR, Intersil and Intel.

After joining Santa Clara University (SCU), U.S., he was initially an adjunct professor before becoming a lecturer and full-time assistant professor. He spent eight years at SCU, serving as the SCU Center for Analog Design and Research director.

Al-Attar joined KAUST in 2014 as a consultant/visiting associate professor and in 2018 as a senior lecturer in the Electrical and Computer Engineering (ECE) program.

Al-Attar’s interests focus on four main topics: (1) Microwave devices: IMPATT modeling and scaling in standard CMOS technology and non-linear transmission lines (NLTL); (2) microstrip patch antenna on-chip for wireless and biomedical applications: Microstrip patch antenna efficiency and losses in standard CMOS beyond 50GHz and novel methods of measuring and characterizing on-chip antennas; (3) analog design optimization: SerDes design, bandgap (voltage and current modes), data converters and DC-DC converters; and (4) LDMOS (laterally-diffused metal-oxide semiconductor) and fin field-effect (FinFet) transistor modeling for RF circuit design.

Al-Attar has contributed to two books: one book on CMOS RF ICs and one book on planar microwave engineering.

• Energy-efficient wireless multi hop networks.
• Cross-layer protocol design.

• Applications of stochastic geometry in performance analysis.

   

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.

Her research interests include computer vision and deep learning, with a focus on video understanding and efficient models.