University of Texas at Austin
Omar Ghattas




phone (512) 232-4304

office POB 4.236A

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Omar Ghattas

Core Faculty GSC Faculty

Fletcher Stuckey Pratt Chair in Engineering

Director Optimization, Inversion, Machine Learning, and Uncertainty for Complex Systems

Professor Mechanical Engineering

Centers and Groups

Research Interests

Uncertainty Quantification Inverse Problems Optimization


Dr. Omar Ghattas is Professor of Mechanical Engineering at The University of Texas at Austin and holds the Fletcher Stuckey Pratt Chair in Engineering. He is also the Director of the OPTIMUS (OPTimization, Inverse problems, Machine learning, and Uncertainty for complex Systems) Center in the Oden Institute for Computational Engineering and Sciences. He is a member of the faculty in the Computational Science, Engineering, and Mathematics (CSEM) interdisciplinary PhD program in the Oden Institute, and holds courtesy appoinements in Geological Sciences, Computer Science, and Biomedical Engineering. Before moving to UT Austin in 2005, he spent 16 years on the faculty of Carnegie Mellon University.

He holds BSE (civil and environmental engineering) and MS and PhD (computational mechanics) degrees from Duke University. With collaborators, he received the ACM Gordon Bell Prize in 2003 (for Special Achievement) and again in 2015 (for Scalability), and was a finalist for the 2008, 2010, and 2012 Bell Prizes. He received the 2019 SIAM Computational Science and Engineering Best Paper Prize, and the 2019 SIAM Geosciences Career Prize. He is a Fellow of the Society for Industrial and Applied Mathematics (SIAM), and serves on the National Academies Committee on Applied and Theoretical Statistics. He is director of the M2dt Center, a DOE ASCR-funded multi-institutional collaboration developing the mathematical foundations for digital twins and serves as co-PI and Chief Scientist for TACC’s Frontera HPC system. 

Ghattas’s research focuses on advanced mathematical, computational, and statistical theory and algorithms for large-scale inverse and optimization problems governed by models of complex engineered and natural systems. He and his group are developing algorithms to overcome the challenges of Bayesian inverse problems, Bayesian optimal experimental design, and stochastic optimal control and design for large-scale complex systems. To do this, they develop structure-exploiting methods for dimesion reducation, surrogates, and neural network approximation, along with high performance computing algorithms. All of these components are integrated and coupled together to form frameworks for digital twins. Driving applications include those in geophysics and climate science (ice sheet dynamics, ice-ocean interaction, seismology, subsurface flows, poroelasticity tsunamis), advanced materials and manufacturing processes (metamaterials, nanomaterials, additive manufacturing), and gravitational wave inference. 

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