Omar Ghattas

About

Omar Ghattas is the John A. and Katherine G. Jackson Chair in Computational Geosciences, a Professor of Geological Sciences and Mechanical Engineering, and the Director of the Center for Computational Geosciences at the Institute for Computational Engineering and Sciences (ICES) at The University of Texas at Austin. He also serves as a faculty member in the Computational Science, Engineering, and Mathematics (CSEM) interdisciplinary Ph.D program in ICES, and holds courtesy appointments in Computer Science, Biomedical Engineering, the Institute for Geophysics, and the Texas Advanced Computing Center. Prior to his tenure at UT Austin starting in 2005, he was a professor at Carnegie Mellon University for 16 years. Ghattas earned his BS, MS, and Ph.D degrees from Duke University in 1984, 1986, and 1988 respectively. His research interests encompass the simulation and modeling of complex geophysical, mechanical, and biological systems on supercomputers, with a particular focus on inverse problems and uncertainty quantification for large-scale systems. His center's current research includes large-scale forward and inverse modeling of Earth's mantle convection, seismic wave propagation, polar ice sheet dynamics, and subsurface flows, employing advanced computational, mathematical, and statistical techniques to address the challenges of complex problems on parallel supercomputers. Ghattas has received numerous awards for research excellence, including the 1998 Allen Newell Medal,…

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Mathematics
• Political Science
• Applied mathematics
• Management science
• Algorithm
• Mathematical optimization
• Physics

Selected publications

• Frontera: The Evolution of Leadership Computing at the National Science Foundation

  Practice and Experience in Advanced Research Computing · 2020 · 172 citations

  As part of the NSF’s cyberinfrastructure vision for a robust mix of high capability and capacity HPC systems, Frontera represents the most recent evolution of trans-petascale resources available to all open science research projects in the U.S. Debuting as the fifth largest supercomputer in the world, Frontera represents a robust and well-balanced HPC system designed to enable large-scale, productive science on day one of operations. The system provides a primary compute capability of nearly 39P…

  PublisherDOI

• Learning physics-based models from data: perspectives from inverse problems and model reduction

  Acta Numerica · 2021 · 144 citations

  1st authorCorresponding

  This article addresses the inference of physics models from data, from the perspectives of inverse problems and model reduction. These fields develop formulations that integrate data into physics-based models while exploiting the fact that many mathematical models of natural and engineered systems exhibit an intrinsically low-dimensional solution manifold. In inverse problems, we seek to infer uncertain components of the inputs from observations of the outputs, while in model reduction we seek l…

  DOI

• Derivative-informed projected neural networks for high-dimensional parametric maps governed by PDEs

  Computer Methods in Applied Mechanics and Engineering · 2021 · 52 citations

  Senior authorCorresponding
  DOI

• Resin percolation and intimate contact in fast processing of thermoplastic composites

  Composites Part A Applied Science and Manufacturing · 2024-03-15 · 24 citations

  article
  PublisherDOI

• ONE- VS TWO- OR THREE-DIMENSIONAL EFFECTS IN SEDIMENTARY VALLEYS

  2025-12-18 · 16 citations

  articleOpen access

  This study of the effects of local geological conditions on seismic ground motion uses 1D amplification as a reference point and examines, via simple theoretical and more realistic numerical examples and observations, how 2D and 3D conditions differ from 1D estimations. Because 1D simulations cannot model basin and edge effects, 1D response tends, in general, to exhibit lower peaks and be of shorter duration than 2D and 3D results. On the other hand, due to destructive interference of different…

  PublisherOA PDFDOI

Recent grants

• MRI: Acquisition of a High Performance Computing System for Online Simulation

  NSF · $800k · 2006–2009

• ITR: Collaborative Research - ASE - (sim+dmc): Image-based Biophysical Modeling: Scalable Registration and Inversion Algorithms and Distributed Computing

  NSF · $78k · 2004–2010

• CDS&E: Collaborative Research: A Bayesian inference/prediction/control framework for optimal management of CO2 sequestration

  NSF · $140k · 2015–2017

Frequent coauthors

• Georg Stadler

  68 shared

• Noémi Petra

  University of California, Merced

  36 shared

• Tan Bui‐Thanh

  32 shared

• Umberto Villa

  The University of Texas at Austin

  32 shared

• Carsten Burstedde

  University of Bonn

  30 shared

• Peng Chen

  29 shared

• Thomas O’Leary-Roseberry

  The University of Texas at Austin

  29 shared

• Karen Willcox

  The University of Texas at Austin

  29 shared

Labs

• Advanced Power Systems and Control Laboratory (APSCL)PI

Education

• Ph.D.

  Duke University

  1988

• M.S.

  Duke University

  1986

• B.S.

  Duke University

  1984

Awards & honors

• 1998 Allen Newell Medal for Research Excellence
• 2004/2005 CMU College of Engineering Outstanding Research Pr…
• SC2002 Best Technical Paper Award
• 2003 IEEE/ACM Gordon Bell Prize for Special Accomplishment i…
• SC2006 HPC Analytics Challenge Award

Similar researchers at University of Texas at Austin

• Jonathan Bardh-65
• Zhantao Chenh-58
• George Birosh-48