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 appointments 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 & 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 & design for
large-scale complex systems. To do so, they develop
structure-exploiting methods for dimension reduction, 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.