Profiles

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.

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.

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.

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 Maurizio Filippone received his Master’s in Physics and a Ph.D. in Computer Science from the University of Genova, Italy, in 2004 and 2008, respectively. During his Ph.D. studies in 2007, Filippone spent a year as a research scholar at George Mason University, U.S.

From 2008 to 2011, he was a research associate at the University of Sheffield, U.K. (2008 to 2009), the University of Glasgow, U.K. (2010), and University College London, U.K. (2011). In 2011, Filippone took up a lecturer position at the University of Glasgow, which he left in 2015 to join EURECOM, France, as an associate professor.

In 2024, Filippone joined the Statistics program at KAUST as an associate professor.

Professor Filippone’s primary focus is Bayesian statistics, which enables sound decision-making through uncertainty quantification in model parameters and predictions; his main interests are in models based on deep learning and Gaussian processes.

Filippone is interested in the foundations of Bayesian statistics and computational aspects related to its use in practice. More specifically, he is developing approximations that enable recover tractability while being principled, practical and scalable.

He is also interested in applications in life and environmental sciences where uncertainty matters.

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.

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 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.

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.

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.

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 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.

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

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.

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.

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.

Eman Kabbas earned a Bachelor of Science and Education in Mathematics from Imam Abdulrahman Bin Faisal University and a Master’s in Mathematics from the University of North Carolina at Charlotte. Her academic journey, marked by deep curiosity and dedication, led her to become a lecturer at Jubail Industrial College (JIC). Now, as a Ph.D. candidate in Applied Mathematics and Computational Sciences under the mentorship of Professor Håvard Rue, Eman delves into Bayesian and computational statistics, striving to bridge theoretical concepts with practical applications. Eman is dedicated to fostering a new way of teaching statistics and data science through her research experience.

Eman Kabbas's research interests focus on developing and applying spline models in non-parametric regression. She addresses the limitations of splines in prediction tasks with insufficient data by proposing a spline model suitable for both regular and irregular observations, leverages Bayesian techniques to ensure efficient modeling and reliable predictions.