Omar Ghattas, professor in the Walker Department of Mechanical Engineering and the Oden Institute for Computational Engineering and Sciences, is the recipient of the 2025 Ivo & Renata Babuška Prize. He received the award “for groundbreaking interdisciplinary contributions to the theory and algorithms of Bayesian inverse problems, and their high-impact application across the geosciences.”
Established in 2022 through a generous gift from Ivo and Renata Babuška, SIAM awards this prize every two years in recognition of high-quality interdisciplinary work that targets any aspect of modeling and numerical solution of a specific engineering or scientific application, including mathematical modeling, numerical analysis, algorithms, and validation. This will be the first time that the prize will be awarded.
Dr. Ghattas earned his Bachelor of Science in Engineering in civil and environmental engineering, as well as his Master of Science and Ph.D. in computational mechanics, from Duke University. He spent 16 years as a faculty member at Carnegie Mellon University before joining the University of Texas at Austin in 2005.
His research contributions have been recognized with numerous accolades. With collaborators, he received the ACM Gordon Bell Prize for Special Achievement (2003) and again for scalability (2015), and was a finalist for the award in 2008, 2010, and 2012. He received the 2019 SIAM Activity Group on Computational Science and Engineering Best Paper Prize and the 2019 SIAM Activity Group on Geosciences Career Prize. He was named a 2014 SIAM Fellow and serves on the National Academies Committee on Applied and Theoretical Statistics.
Dr. Ghattas is also the director of the Mathematics of Machine Learning, Modeling, and Data for Digital Twins Center, a multi-institutional collaboration funded by the Department of Energy’s Advanced Scientific Computing Research program, which focuses on developing the mathematical foundations for digital twins. Additionally, he serves as Co-Principal Investigator and Chief Scientist for the Texas Advanced Computing Center’s Frontera high-performance computing system.
Dr. Ghattas’s research focuses on advancing mathematical, computational, and statistical theory and algorithms for large-scale inverse and optimization problems governed by models of complex engineered and natural systems. His group develops algorithms to address challenges in Bayesian inverse problems, Bayesian optimal experimental design, and stochastic optimal control and design for large-scale systems. Their work involves structure-exploiting methods for dimension reduction, surrogate modeling, and neural network approximation, integrated with high-performance computing techniques. These components are coupled to form frameworks for digital twins, with applications spanning geophysics and climate science—including ice sheet dynamics, ice-ocean interaction, seismology, subsurface flows, poroelasticity, and tsunamis—as well as advanced materials and manufacturing processes such as metamaterials, nanomaterials, and additive manufacturing. His research also extends to gravitational wave inference.
