High-Performance Computing and Big Data

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.

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.

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 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.

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.

Hong G. Im received his B.S. and M.S. in Mechanical Engineering from Seoul National University, and Ph.D. in Mechanical and Aerospace Engineering from Princeton University. He spent two years as a Research Fellow at the Center for Turbulence Research, Stanford University, followed by a post-doctoral tenure at the Combustion Research Facility, Sandia National Laboratories. He was appointed as Assistant/Associate/Full Professor at University of Michigan in the Mechanical Engineering Department from 2000 to 2014. In 2013, he joined KAUST as a Professor of Mechanical Engineering.

Computational methods for reacting flows; direct numerical simulation; large eddy simulation; turbulent combustion modeling; internal combustion engines; spray modeling; alternative fuels and utilization; pollutant reduction and control; combustion chemistry; combustion synthesis of materials; combustion at extreme conditions; low grade fuel combustion; plasma and electric field effects on flames, cryogenic carbon capture.