University of Texas at Austin
Omar Ghattas

Contact

websitehttps://scholar.google.com/citations?hl=en&authuser=1&user=A5vhsIYAAAAJ

email

phone (512) 232-4304

office POB 4.236A

Omar Ghattas

Core Faculty GSC Faculty

John A. and Katherine G. Jackson Chair in Computational Geosciences

Director Center for Computational Geosciences and Optimization

Professor Walker Department of Mechanical Engineering

Professor Jackson School of Geosciences

Research Interests

Uncertainty Quantification Inverse Problems Optimization

Biography

Dr. Omar Ghattas is a Professor of Geological Sciences and Mechanical Engineering at The University of Texas at Austin. He is also the Director of the Center for Computational Geosciences and Optimization in the Oden Institute for Computational Engineering and Sciences and holds the John A. and Katherine G. Jackson Chair in Computational Geosciences. 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 appointments in 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 & Engineering Best Paper Prize, and the 2019 SIAM Geosciences Career Prize. He is a Fellow of the Society for Industrial and Applied Mathematics (SIAM).

Ghattas's research focuses on advanced mathematical, computational, and statistical theory and algorithms for complex high-dimensional inverse and optimization problems in computational science and engineering. His current methodological research is in the areas of inverse problems, Bayesian inference, optimal experimental design, uncertainty quantification, stochastic optimization and optimal control, machine learning, model reduction, and scalable parallel algorithms for these problems as well as the forward PDE models that underlie them. Scientific areas of interest include (1) geophysics and climate science (ice sheet dynamics, ice-ocean interaction, mantle convection, seismology, subsurface flows, poroelasticity, tsunamis), (2) advanced materials and manufacturing processes (metamaterials, nanomaterials, additive manufacturing), (3) physics (plasma fusion energy, gravitational wave inference), and (4) infectious disease spread.