Profiles

Diogo Gomes is a professor of Applied Mathematics and Computational Science (AMCS) at KAUST.

He received his Ph.D. in Mathematics in 2000 from the University of California at Berkeley, U.S. Gomes completed his postdoctoral studies at the Institute for Advanced Study, Princeton University, U.S., in 2000, and at the University of Texas at Austin, U.S., in 2001. In 2006, he earned a Habilitation in Mathematics from the Technical University of Lisbon, Portugal.

In recognition of his academic excellence, Gomes was awarded UC Berkeley’s Morrey Prize in 1997. He has served as Editor of Minimax Theory and its Applications and the Journal of Dynamics and Games and Dynamic Games and Applications.

Professor Gomes' work focuses on partial differential equations (PDEs), namely viscosity solutions to elliptic, parabolic and Hamilton-Jacobi equations.

His research encompasses classical PDE questions—such as well-posedness, existence and uniqueness and regularity theory—and numerical methods and their applications. Gomes is particularly interested in applying mean-field game models to social sciences, economics and finance.

Athanasios Tzavaras is a professor in KAUST's Applied Mathematics and Computational Science (AMCS) program, and principal investigator of the Applied Partial Differential Equations (AppliedPDE) research group.

Professor Tzavaras obtained a Diploma in Naval Architecture and Marine Engineering in 1981 from the National Technical University of Athens, Greece. He continued his studies in the United States, earning an M.Sc. in 1983 and a Ph.D. in Applied Mathematics in 1985 from Brown University.

He held academic positions at the University of Wisconsin-Madison from 1987 to 2005, the University of Maryland from 2005 to 2009 and the University of Crete, Greece, from 2002 to 2004 and from 2010 to 2014. Additionally, he has held visiting positions at Purdue University, U.S., Stanford University, U.S., École Polytechnique, France and the Université Marie et Pierre Curie - Paris VI, France.

Tzavaras is a fellow of the European Academy of Sciences. He is a member of the American Mathematical Society (AMS), the Society of Industrial and Applied Mathematics (SIAM) and the International Society for the Interaction of Mechanics and Mathematics. He is Associate Editor of various journals and Corresponding Editor of the SIAM Journal for Mathematical Analysis.

Link to personal website.

Professor Tzavaras' research interests include mathematical modelling, analysis and computation of fluids and materials. He investigates hyperbolic conservation laws and the structure of fluid mechanics and elasticity equations.

Among his other interests are singularity formation in solid mechanics (cavitation and shear bands), multi-scale analysis, hydrodynamic limits and practical properties of fluids, kinetic models of dilute polymeric systems and discrete lattice dynamics.

Daniele Boffi is a professor in the Applied Mathematics and Computational Science Program at KAUST. Before joining KAUST, he spent 14 years as a full professor in the Department of Mathematics at the University of Pavia (UnIPV), Italy.

Boffi received his Ph.D. in Mathematics from UnIPV in 1996 and his M.S. in Mathematics from the same institution in 1990. During his time in Italy, he served as the director of Pavia's Higher Education School and was a member of several academic committees, including the University's Academic Senate and Evaluation Committee.

Boffi's research focuses on the numerical approximation of partial differential equations, spanning various aspects of mathematical modeling and scientific computing. He has made significant contributions to the modeling and simulation of fluid-structure interaction problems and the study of the numerical approximation of eigenvalue problems arising from partial differential equations.

At KAUST, Boffi leads the Numerical Methods for PDEs (NumPDE) research group, which provides a platform for the mathematical analysis and numerical validation of numerical schemes.

Professor Boffi's research is primarily devoted to the numerical approximation of partial differential equations, encompassing various aspects of mathematical modeling and scientific computing.

In particular, he has made significant contributions to the modeling and simulation of fluid-structure interaction problems and the study of the numerical approximation of eigenvalue problems arising from partial differential equations.

He leads the Numerical Methods for PDEs (NumPDE) research group at KAUST, which provides a rigorous platform for the mathematical analysis and numerical validation of numerical schemes.

David Ketcheson is a Professor of Applied Mathematics and Computational Science and the principal investigator of the Numerical Mathematics Group. He received his Ph.D. and M.S. in Applied Mathematics from the University of Washington in 2009 and 2008, respectively. Ketcheson obtained B.S. degrees in Mathematics and Physics & Astronomy from Brigham Young University, U.S., in 2004.

Professor Ketcheson’s research involves the analysis and development of numerical methods for integrating ordinary and partial differential equations (PDEs), as well as the implementation of such methods in open source, accessible, high-performance software and its application to understanding the behaviour of nonlinear waves in heterogeneous materials.

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.

Diogo Gomes is a professor of Applied Mathematics and Computational Science (AMCS) at KAUST.

He received his Ph.D. in Mathematics in 2000 from the University of California at Berkeley, U.S. Gomes completed his postdoctoral studies at the Institute for Advanced Study, Princeton University, U.S., in 2000, and at the University of Texas at Austin, U.S., in 2001. In 2006, he earned a Habilitation in Mathematics from the Technical University of Lisbon, Portugal.

In recognition of his academic excellence, Gomes was awarded UC Berkeley’s Morrey Prize in 1997. He has served as Editor of Minimax Theory and its Applications and the Journal of Dynamics and Games and Dynamic Games and Applications.

Professor Gomes' work focuses on partial differential equations (PDEs), namely viscosity solutions to elliptic, parabolic and Hamilton-Jacobi equations.

His research encompasses classical PDE questions—such as well-posedness, existence and uniqueness and regularity theory—and numerical methods and their applications. Gomes is particularly interested in applying mean-field game models to social sciences, economics and finance.

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.

George Turkiyyah is a research professor in the Applied Mathematics and Computational Science program at KAUST.

Before joining KAUST, he was a professor at the American University of Beirut, where he also served as chair of the computer science department. Prior to joining AUB, he was an assistant professor and later an associate professor at the University of Washington in Seattle.

Turkiyyah earned a Bachelor of Engineering (B.Eng.) in civil and environmental engineering from the American University of Beirut, and both a Master of Science (M.S.) and a Doctor of Philosophy (Ph.D.) in computer-aided engineering from Carnegie Mellon University.

Turkiyyah has been involved in the development of knowledge-based AI systems that have been deployed in practice. He has also developed several widely used simulation codes for high-resolution finite element engineering applications. His work on fast methods for surgical simulation has led to a software startup and several patents.

His research has earned several awards, including the Transportation Research Board K.B. Woods Award in 2003 for best paper in design and construction, the Best Presentation Award at the ACM Solid and Physical Modeling Conference in 2007, and the Best Poster Award at the Medicine Meets Virtual Reality Conference in 2006. 

He chaired the 2003 ASCE Engineering Mechanics Conference and co-chaired the Eighth ACM Symposium on Solid Modeling and Applications (SPM) in 2003. He is a member of ACM and the Society for Industrial and Applied Mathematics (SIAM).

Professor Turkiyyah’s current research interests include hierarchically low-rank matrix algorithms and their HPC/GPU implementations to support the development of simulation models at extreme scales.

His work addresses various applications of hierarchical matrix technology, including PDE-constrained optimization, high-dimensional statistics problems, multi-dimensional fractional diffusion problems, scientific data compression and second-order methods for training neural networks.

Professor Xu is a leading figure in the development, design and analysis of fast methods for finite element discretization and large-scale equation solutions. He has made many groundbreaking contributions in these areas, including several fundamental theories and algorithms that bear his name. These include the Bramble-Pasciak-Xu (BPX) preconditioner, the Hiptmair-Xu (HX) preconditioner and the Xu-Zikatanov (XZ) identity.

Xu received his Bachelor's degree from Xiangtan University, China, in 1982, his Master's degree from Peking University, China, in 1984 and his doctoral degree from Cornell University, U.S., in 1989. He joined Pennsylvania State University (Penn State), U.S., in 1989 as Assistant Professor of Mathematics, was promoted to associate professor in 1991, and to professor in 1995.

He was named a Distinguished Professor of Mathematics in 2007, the Francis R. and Helen M. Pentz Professor of Science in 2010 and the Verne M. Willaman Professor of Mathematics in 2015 at Penn State. He was also awarded the first Feng Kang Prize for Scientific Computing in 1995 and the Humboldt Award for senior U.S. scientists in 2005. His work was featured as one of the "Top 10 breakthroughs in computational science" in a 2008 US Department of Energy report.

According to Google Scholar, Xu has published more than 240 scientific papers with more than 18,500 citations. He was a plenary speaker at the International Congress for Industrial and Applied Mathematics in 2007 and an invited speaker at the International Congress for Mathematicians in 2010.

Xu serves on the editorial boards of many influential journals in computational mathematics and co-edits numerous research monographs and conference proceedings. He has organized or served as a scientific committee member for more than 100 international conferences, workshops and summer schools.

He is a Fellow of the Society for Industrial and Applied Mathematics (SIAM), the American Mathematical Society (AMS), the American Association for the Advancement of Science (AAAS) and the European Academy of Sciences (EurASc). In 2023, he was elected to the prestigious Academia Europaea.

Dr. Xu’s research focuses on numerical methods for partial differential equations and big data, specifically finite element methods, multigrid methods and deep neural networks for their theoretical analysis, algorithmic development and practical applications.

Recently, he has devoted himself to mathematical studies of deep learning, working on topics such as the approximation theory of deep neural networks. He has also been developing convolutional neural networks and training algorithms from the multigrid viewpoint and subspace corrections method.

Katerina Nik is an assistant professor of Applied Mathematics and Computational Sciences (AMCS) in the Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division at KAUST. Her work focuses on partial differential equations that describe phenomena in biological growth processes, fluid dynamics, and mechanical engineering. She joined KAUST in 2024 after contributing to research at prominent European institutions.

Prior to joining KAUST, Dr. Nik worked as a principal investigator at the Austrian Academy of Sciences, supported by the APART-MINT Fellowship, which enabled her to lead innovative research in applied mathematics. Before this, she was a postdoctoral researcher at the Delft Institute of Applied Mathematics, TU Delft, where she collaborated with Professor Manuel Gnann's group. She also served as a postdoctoral researcher at the Faculty of Mathematics, University of Vienna, Austria, working with Professor Ulisse Stefanelli from 2020 to 2024.

Earlier in her career, she was a research and teaching assistant at Leibniz University Hannover (LUH), Germany, where she completed her doctorate in mathematics. Her academic contributions during this period focused on nonlinear dynamics and the modeling of complex systems.

Professor Nik works on (nonlinear) partial differential equations that describe phenomena in biological growth processes, fluid dynamics, and mechanical engineering. Additionally, she is interested in modeling with differential equations. In order to solve these problems, Nik employs both applied and pure mathematics methods. Her main research topics include:

• Nonlinear evolution equations and operator semigroups
• Free boundary problems
• Calculus of variations
• Well-posedness and qualitative properties of solutions
• Nonlinear dispersive waves
• Thin fluid film equations
• Microelectromechanical systems (MEMS)
• Biological growth processes, such as volumetric and surface growth

Al-Khawarizmi Distinguished Professor of the KAUST Statistics Program, Marc G. Genton, is a specialist in spatial and spatio-temporal statistics with environmental applications. His work has revolutionized environmental data science, addressing large-scale problems involving spatial and temporal datasets. To emulate climate model outputs of more than one billion temperature data points, he developed 3-D space-time stochastic generators using spectral methods and fast Fourier transforms.

Genton is a fellow of the American Statistical Association, the Institute of Mathematical Statistics, the American Association for the Advancement of Science, and an elected member of the International Statistical Institute (ISI).

In 2010, he received the El-Shaarawi Award for Excellence from the International Environmetrics Society (TIES) and the Distinguished Achievement Award from the Section on Statistics and the Environment (ENVR) of the American Statistical Association (ASA). In 2017, he was honored with the Wilcoxon Award for Best Applications Paper in Technometrics. He received an ISI Service Award in 2019 and the Georges Matheron Lectureship Award in 2020 from the International Association for Mathematical Geosciences (IAMG).

He led a Gordon Bell Prize finalist team with the ExaGeoStat software at Supercomputing 2022. In 2023, he was awarded the Royal Statistical Society’s (RSS) Barnett Award for his outstanding contributions to environmental statistics. He also received the prestigious 2024 Don Owen Award from the San Antonio Chapter of the American Statistical Association and led a Gordon Bell Prize finalist team in Climate Modeling for the Exascale Climate Emulator at SC24.

In addition to authoring over 300 publications, Genton has edited a book on skew-elliptical distributions and their applications. He has given more than 400 presentations at conferences and universities worldwide.

Genton received his Ph.D. in statistics in 1996 from the Swiss Federal Institute of Technology Lausanne (EPFL), Switzerland. He also holds an M.S. degree in applied mathematics teaching, earned in 1994 from EPFL.

Before joining KAUST, he held prominent faculty positions at the Massachusetts Institute of Technology (MIT), North Carolina State University, the University of Geneva and Texas A&M University.

Professor Genton’s research centers around spatial and spatio-temporal statistics, including the statistical analysis, visualization, modeling, prediction and uncertainty quantification of spatio-temporal data. A wide range of applications can be found in environmental and climate science, renewable energies, geophysics and marine science.

Currently, he is developing high-performance computing tools for spatial statistics and expanding the capabilities of ExaGeoStat, the software developed by his Spatio-Temporal Statistics and Data Science (STSDS) research group and the Extreme Computing Research Center (ECRC).

An in-depth, five-year study of wind energy potential in Saudi Arabia, led by Genton, culminated in a comprehensive plan for developing the Kingdom's future wind energy strategy. With the help of apps and 3-D glasses, he has also demonstrated how virtual reality can help visualize environmental data on smartphones.

Professor Matteo Parsani received his Master’s in Aerospace Engineering in 2006 from Politecnico di Milano, Italy, and his Ph.D. in Mechanical Engineering in 2010 from Vrije Universiteit, Belgium.

Parsani’s journey at KAUST began when he joined the University as a postdoctoral fellow in 2010. Four years later, while pursuing a postdoctoral fellowship at NASA’s Langley Research Center in the United States, he received an offer to return to KAUST as a professor.

He is now an associate professor of Applied Mathematics and Computational Science in the Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division and the principal investigator of the Advanced Algorithms and Simulations Lab (AANSLab). Parsani is also affiliated with the Mechanical Engineering Program at KAUST.

His research focuses on developing self-adaptive, variable-order, robust algorithms for compressible flows and advection-reaction-diffusion, designing efficient simulation codes and deploying them on large parallel platforms.

Parsani's high-performance computational solvers and libraries are utilized to tackle complex engineering challenges in collaboration with industry partners such as Boeing, NASA’s Langley Research Center (LaRC), the McLaren F1 racing team, Airbus, E1 Series and Lucid Motors.

Professor Matteo Parsani’s research interests are related to designing and implementing novel, robust and scalable numerical methods. Specifically, unstructured grids for hyperbolic and mixed hyperbolic/parabolic partial differential equations.

A core focus of Parsani’s research is on efficient and robust algorithms for the aerodynamic and aeroacoustic design of aerospace vehicles. Additionally, he studies non-classical gas-dynamic phenomena for energy conversion systems and the investigation of biological flow in cancer treatments.

His current research examines the stability and efficiency of spatial and temporal discretizations and structure-preserving methods that can mimic results from the continuous to the discrete level. A number of application domains are currently driving his research, including computational aerodynamics, dense gas flow simulations, and computational aeroacoustics.

Professor Miguel Urbano, who joined KAUST in 2022, received his Ph.D. in Mathematical Analysis in 1999 from the University of Lisbon, Portugal. Following a postdoctoral position at Northwestern University in the United States, he became an assistant professor at the University of Coimbra (UC), Portugal. He was promoted to associate professor with tenure in 2004 at UC and awarded a habilitation in mathematics in 2005 before becoming a full professor in 2009.

Professor Urbano is the author of The Method of Intrinsic Scaling, published in the Lecture Notes in Mathematics series, and over 70 scientific papers on nonlinear partial differential equations (PDEs). He has served on panels evaluating grants and research projects for the European Union, the European Research Council, the Academy of Finland, the Latvian Council of Science, the Serrapilheira Institute of Brazil and the Portuguese Science Foundation.

Urbano served on Portugal's National Council for Science and Technology from 2012 to 2015, won the José Anastácio da Cunha Prize from the Portuguese Mathematical Society in 2002, and was an associate editor for Nonlinear Analysis from 2013 to 2021. He is a corresponding academician of the Lisbon Academy of Sciences (elected in January 2021) and has been the editor-in-chief of Portugaliae Mathematica since January 2022.

Professor Miguel Urbano is an expert on free boundary problems and regularity theory for nonlinear PDEs, particularly on the method of intrinsic scaling for singular or degenerate-type equations.

He has made several contributions leading to a better understanding of the local behaviour of weak solutions, e.g., the derivation of a quantitative modulus of continuity for weak solutions of the two-phase Stefan problem, which models a phase transition at a constant temperature or the proof of a long-standing conjecture on the optimal regularity for solutions of the p-Poisson equation in the plane.

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.

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.

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.

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.

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

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.

Andrea Fratalocchi is a full professor in the Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division at KAUST. He joined the University in January 2011 as an assistant professor and was promoted to associate professor in 2016. He is one of the founders of the CEMSE Division and the principal investigator of the Primalight Lab.

Before joining KAUST, Fratalocchi was a research fellow at Sapienza University of Rome under a KAUST Fellowship Award. From 2007 to 2009, he was a postdoctoral researcher at Sapienza University under a “New Talent” Award from the Enrico Fermi Research Center. He obtained a Master of Science in Electrical Engineering in 2003 and a Ph.D. in Electrical Engineering in 2007 from the University of Roma Tre, Italy.

Fratalocchi’s career is marked by numerous accolades, including the GCC Enterprise Awards for Best Electrical Engineer of the Year in 2017, the Journal of Optics Outstanding Referee Award in 2017, the Nature Exceptional Referee Award in 2015, and an entry into the Guinness World Records for developing the “Darkest Material Made by Mankind” in 2015.

In 2019, he became a Fellow of the Institute of Physics (IOP), a Senior Member of the IEEE, and a Fellow of the Optical Society of America (OSA).

Fratalocchi has authored over 200 publications, including three books and six patents. He ranks in the top 2% of optics researchers worldwide, based on the standardized citation index compiled by PLOS.

Professor Fratalocchi is dedicated to advancing the field of physics and engineering. His research approach harnesses the potential of complex physical systems, characterized by many degrees of freedom, turning them from theoretical challenges into real-world technological solutions with diverse applications.

His research embraces a nonlinear paradigm, departing from traditional "cause and effect" or linear thinking. This approach finds applications in diverse areas such as chaos theory, rare events, brain functions, natural mimicry and camouflage, swarm's cooperative dynamics and intelligence.

Using disorder as a building block, he proposes novel, low-cost, scalable technologies that outperform current systems by several orders of magnitude.

As part of his engineering research, he has developed world-record-performing nanomaterials for concentrating solar power, steam generation, desalination, solar water splitting, solar and chemical fuel production for carbon-negative technologies, artificial intelligence optical neural networks for hyperspectral imaging and sensing, and machine-learning nanomaterials for wave control and bioimaging, including early disease detection of cancer and diabetes.

Professor David Bolin joined KAUST in 2019 as an Associate Professor of Statistics and Affiliate Professor of Applied Mathematics and Computational Sciences (AMCS).

Bolin received both his Ph.D. degree in Mathematical Statistics and M.Sc. in Engineering Mathematics from Lund University, Sweden, in 2012 and 2007, respectively.

Upon completing his Ph.D., he spent one year at Umeå University, Sweden, working as a postdoctoral fellow before moving to the Chalmers University of Technology, Sweden, as an Assistant Professor.

In 2016, Bolin became an Associate Professor of Mathematical Statistics at the University of Gothenburg, Sweden, where a year later, he received the title of Docent in Mathematical Statistics.

Professor Bolin’s main research interests are stochastic partial differential equations (PDEs) and their applications in statistics, focusing on developing practical, computationally efficient tools for modeling non-stationary and non-Gaussian processes.

The Swedish researcher leads the Stochastic Processes and Applied Statistics (StochProc) research group at KAUST, which focuses on statistical methodology for stochastic processes and random fields based on stochastic PDEs.

This research combines methods from statistics, probability and applied mathematics in order to construct more flexible statistical models and better computational methods for statistical inference. In parallel with the theoretical research, the group works on applications in a wide range of areas, ranging from brain imaging to environmental sciences.

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 Rue earned his Ph.D. in 1993 from the Norwegian University of Science and Technology. He began his academic career at the same institution in 1994 and was promoted to full professor in 1997. He has also held adjunct positions at the Norwegian Computing Center and the Arctic University of Norway. Rue is an elected member of the Norwegian Academy of Science and Letters, the Royal Norwegian Society of Science and Letters, the Norwegian Academy of Technological Sciences and the International Statistical Institute.

Upon joining KAUST in 2017, Rue established the Bayesian Computational Statistics & Modeling research group. The group develops efficient Bayesian inference schemes and tools to improve Bayesian inference and modeling using latent Gaussian models. He received the Guy Medal in Silver from the Royal Statistical Society in 2021 for his groundbreaking work in this area.

Professor Rue’s research interests lie in computational Bayesian statistics and Bayesian methodology, such as priors, sensitivity and robustness. His main body of research is built around the R-INLA project—a project aimed at providing a practical way to analyze latent Gaussian models at extreme data scales using approximate Bayesian analysis. The work also includes efforts to model Gaussian fields with stochastic partial differential equations, which are applied to spatial statistics.

Hernando Ombao is a professor in the Statistics Program and the principal investigator of the Biostatistics Group at KAUST. His research focuses on developing time series models and novel data science methods for analyzing high-dimensional complex biological processes. He leads a group of researchers specializing in spectral and time-series analysis, functional data analysis, state-space models, and signal processing for brain signals and images. His group collaborates closely with neuroscientists to model the associations between neurophysiology, cognition and animal behavior.

Before joining KAUST, Professor Ombao was a tenured faculty member at the University of Illinois Urbana-Champaign, U.S., Brown University, U.S. and the University of California, Irvine, U.S. He earned a B.Sc. in Mathematics in 1989 from the University of the Philippines, an M.Sc. in Statistics in 1995 from the University of California, Irvine, and a Ph.D. in biostatistics in 1999 from the University of Michigan.

Ombao is an elected fellow of the American Statistical Association. He has been awarded several grants as a principal investigator by the U.S. National Science Foundation. In 2017, he received the UC Irvine School of Information Sciences Mid-Career Award for Research. He has served as a panel member of the Biostatistics Study Section at the U.S. National Institutes of Health and as an associate editor of leading statistical journals. He is co-editor of the book Handbook of Statistical Methods for Neuroimaging (CRC Press, 2016) and co-editor of a special issue of the Journal of Time Series Analysis.

At KAUST, he holds secondary appointments in the Applied Mathematics and Computational Sciences (AMCS) and the Bioengineering Programs. He also serves as chair of the Institutional Biosafety and Bioethics Committee. Ombao actively collaborates with researchers across the campus and is a co-founder of the interdisciplinary KAUST Neuro-AI Laboratory (NAIL).

Professor Ombao’s research focuses on the statistical modeling of time series data and the visualization of high-dimensional signals and images.

He has developed a coherent set of methods for modeling and inference on the dependence of complex brain signals: testing for differences in networks across patient groups, identifying biomarkers, classifying diseases based on networks and modeling associations between high-dimensional data from different domains, such as genetics, brain function and behavior.

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.

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

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

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

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

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

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

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

Professor 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 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.

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.

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,

Erik von Schwerin's research interests include Deterministic and stochastic differential equations, Computations with uncertainty, Error control and adaptivity, Systematic coarse graining, Hybrid modeling, and Multiscale methods.

In 2019, Dr. Ruzayqat received his PhD in Mathematics from the University of Tennessee-Knoxville, USA. In 2012, he received a Bachelor degree in Physics and mathematics from Birzeit University, Palestine. Dr. Hamza Ruzayqat joined KAUST in November 2019 as a Post-Doctoral Research Fellow in the group of Computational Probability (COMPPROB). Late in 2022, he was promoted to Research Scientist and now a member in Omar Knio's Research Group. 

Dr. Ruzayqat main research is focused on Monte Carlo algorithms, data assimilation and uncertainty quantification. In particular, he is working on particle filters, SMCMC filters, unbiased estimators, inverse problems, parameter estimation and Bayesian inference in discrete/continuous-time, linear/nonlinear, low or high dimensional state-space models. In the past he worked on off-lattice kinetic Monte Carlo methods for atomic simulations.

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

Mohamed Farhat is currently a Research Scientist at King Abdullah University of Science and technology (KAUST), Thuwal, Saudi Arabia. 

Dr. Mohamed Farhat received his Ph.D. in Optics and Electromagnetism from Aix-Marseille University where he obtained as well his Master degree in Theoretical Physics. His PhD dissertation was titled by “Metamaterials for Harmonic and Biharmonic Cloaking and Superlensing.” He has authored over 100 publications, including 1 edited book, 98 journal papers, 7 book chapters, and 5 international patents, as well as over 90 conference papers, with over 5100 citations, as of November 2023. He has organized several special sessions at the Meta conferences, and is active reviewer for many international journals in Physics including Physical Review Letters and Nature Physics. He has co-edited the book “Transformation Wave Physics: Electromagnetics, Elastodynamics and Thermodynamics” at Pan Stanford Publishing. 

His research is in the fields of plasmonics and metamaterials with applications spanning optical and acoustical waves.

Rafayel Teymurazyan obtained his PhD in Mathematics from the University of Lisbon (Portugal) in 2013. After postdoc and research positions at the Federal University of Ceará (Brazil), the University of Texas at Austin (USA) and the University of Coimbra (Portugal), he joined KAUST in May of 2023. He works on regularity theory for nonlinear PDEs and the mathematical analysis of free boundary problems.

Rafayel Teymurazyan works on nonlinear  partial differential equations (PDEs) and free boundary problems. The term free boundary problem (FBP) refers to a PDE to be solved both for an unknown function and for an unknown domain. FBPs arise in range of mathematical models that are used to describe a physical or biological phenomenon (for example, ice melting into water, population dynamics), or an economical or financial occurrence (American options, stock markets).

Dr. Sameh Abdulah is an HPC research scientist specializing in high-performance computing (HPC), and large-scale data analytics. He is a Research Scientist at the Computer, Electrical and Mathematical Sciences and Engineering Division at KAUST. His work focuses on developing scalable algorithms and efficient software frameworks to address complex computational challenges across diverse scientific and engineering domains, including spatial statistics.

He serves as a key link between three major research groups within the extreme computing research at KAUST: the Hierarchical Computations on Manycore Architectures (HiCMA) group led by Professor David Keyes, the Spatio-Temporal Statistics & Data Science (STSDS) group led by Professor Marc Genton, and the Environmental Statistics (ES) group led by Professor Ying Sun. His primary role is to bridge advanced parallel linear algebra (LA) innovations with high-performance computing (HPC) in the spatial statistics field in the context of climate and weather applications.

Dr. Abdulah was honored with the ACM Gordon Bell Prize for Climate Modelling in November 2024. His team's pioneering work in climate simulation set new benchmarks in computational efficiency and resolution, transforming how climate data is modeled and analyzed. He was also part of the KAUST team nominated for the ACM Gordon Bell Prize in the general track for spatial data modeling/prediction in 2022.

He has significantly contributed to scalable matrix computations, particularly in designing numerical libraries that leverage modern hardware architectures. His expertise includes mixed-precision matrix computations, geostatistical modeling, and prediction. He has also developed cutting-edge methodologies for accelerating data-intensive simulations, enabling transformative weather/climate modeling advancements.

As a passionate advocate for open-source software, Dr. Abdulah is actively involved in collaborative research and software development, sharing tools and libraries that empower researchers globally. His work is driven by a commitment to innovation and interdisciplinary collaboration, harnessing the power of HPC to tackle some of the most pressing challenges in computational science.

Adding the HPC capabilities to existing science is a big challenge. Statistics has a huge number of tools and methods that can be more attractive if they scaled up. Dr Abdulah is doing this by working through two different groups to transfer knowledge and experience between two different views of the same problem. In other words, he is moving the traditional statistical tools and methods to the HPC era.