World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
59
Citations
12177
World Ranking
597
National Ranking
305

Engineering and Technology

D-Index
64
Citations
14115
World Ranking
1666
National Ranking
541

Omar Ghattas publication distribution in Mathematics in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Mathematics in 2026. The highlighted bar marks where Omar Ghattas sits on this spectrum.

42–46 publications: 3 scientists 47–51 publications: 5 scientists 52–56 publications: 7 scientists 57–61 publications: 20 scientists 62–66 publications: 14 scientists 67–71 publications: 25 scientists 72–76 publications: 19 scientists 77–81 publications: 35 scientists 82–86 publications: 50 scientists 87–91 publications: 60 scientists 92–96 publications: 86 scientists 97–101 publications: 84 scientists 102–106 publications: 83 scientists 107–111 publications: 90 scientists 112–116 publications: 99 scientists 117–121 publications: 90 scientists 122–126 publications: 91 scientists 127–131 publications: 109 scientists 132–136 publications: 110 scientists 137–141 publications: 98 scientists 142–146 publications: 112 scientists 147–151 publications: 102 scientists 152–156 publications: 88 scientists 157–161 publications: 106 scientists 162–166 publications: 83 scientists 167–171 publications: 102 scientists 172–176 publications: 77 scientists 177–181 publications: 81 scientists 182–186 publications: 78 scientists 187–191 publications: 71 scientists 192–196 publications: 92 scientists 197–201 publications: 64 scientists 202–206 publications: 69 scientists 207–211 publications: 64 scientists 212–216 publications: 62 scientists 217–221 publications: 58 scientists 222–226 publications: 53 scientists 227–231 publications: 50 scientists 232–236 publications: 46 scientists 237–241 publications: 46 scientists 242–246 publications: 46 scientists 247–251 publications: 43 scientists 252–256 publications: 29 scientists 257–261 publications: 45 scientists 262–266 publications: 30 scientists 267–271 publications: 33 scientists 272–276 publications: 34 scientists 277–281 publications: 30 scientists 282–286 publications: 31 scientists 287–291 publications: 21 scientists 292–296 publications: 34 scientists 297–301 publications: 26 scientists 302–306 publications: 10 scientists 307–311 publications: 17 scientists 312–316 publications: 23 scientists 317–321 publications: 13 scientists 322–326 publications: 16 scientists 327–331 publications: 26 scientists 332–336 publications: 13 scientists 337–341 publications: 13 scientists 342–346 publications: 16 scientists 347–351 publications: 17 scientists 352–356 publications: 12 scientists 357–361 publications: 18 scientists 362–366 publications: 18 scientists 367–371 publications: 9 scientists 372–376 publications: 11 scientists 377–381 publications: 8 scientists 382–386 publications: 8 scientists 387–391 publications: 9 scientists 392–396 publications: 9 scientists 397–401 publications: 8 scientists 402–406 publications: 11 scientists 407–411 publications: 6 scientists 412–416 publications: 6 scientists 417–421 publications: 9 scientists 422–426 publications: 8 scientists 427–431 publications: 5 scientists 432–436 publications: 8 scientists 437–441 publications: 8 scientists 442–446 publications: 4 scientists 447–451 publications: 4 scientists 452–456 publications: 4 scientists 457–461 publications: 2 scientists 462–466 publications: 2 scientists 467–471 publications: 4 scientists 472–476 publications: 3 scientists 477–481 publications: 3 scientists 482–486 publications: 6 scientists 487–491 publications: 3 scientists 492–496 publications: 5 scientists 497–501 publications: 5 scientists 502–506 publications: 1 scientists 507–511 publications: 6 scientists 512–516 publications: 4 scientists 517–521 publications: 1 scientists 522–526 publications: 3 scientists 527–531 publications: 1 scientists 532–536 publications: 4 scientists 537+ publications: 100 scientists
42 publications 537+

This scientist: 196 publications — 61st percentile

61% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 537 publications or more.

Omar Ghattas D-index placement in Mathematics in 2026

The chart shows the D-index (discipline H-index) distribution of Mathematics scientists ranked by Research.com in 2026. The highlighted bar marks where Omar Ghattas sits on this spectrum.

30 D-Index: 174 scientists 31 D-Index: 151 scientists 32 D-Index: 174 scientists 33 D-Index: 117 scientists 34 D-Index: 136 scientists 35 D-Index: 127 scientists 36 D-Index: 145 scientists 37 D-Index: 153 scientists 38 D-Index: 150 scientists 39 D-Index: 150 scientists 40 D-Index: 138 scientists 41 D-Index: 136 scientists 42 D-Index: 93 scientists 43 D-Index: 108 scientists 44 D-Index: 115 scientists 45 D-Index: 112 scientists 46 D-Index: 103 scientists 47 D-Index: 75 scientists 48 D-Index: 59 scientists 49 D-Index: 67 scientists 50 D-Index: 60 scientists 51 D-Index: 57 scientists 52 D-Index: 59 scientists 53 D-Index: 62 scientists 54 D-Index: 60 scientists 55 D-Index: 50 scientists 56 D-Index: 42 scientists 57 D-Index: 54 scientists 58 D-Index: 50 scientists 59 D-Index: 42 scientists 60 D-Index: 41 scientists 61 D-Index: 35 scientists 62 D-Index: 40 scientists 63 D-Index: 21 scientists 64 D-Index: 31 scientists 65 D-Index: 27 scientists 66 D-Index: 29 scientists 67 D-Index: 19 scientists 68 D-Index: 25 scientists 69 D-Index: 17 scientists 70 D-Index: 18 scientists 71 D-Index: 12 scientists 72 D-Index: 14 scientists 73 D-Index: 13 scientists 74 D-Index: 18 scientists 75 D-Index: 9 scientists 76 D-Index: 11 scientists 77 D-Index: 10 scientists 78 D-Index: 9 scientists 79 D-Index: 16 scientists 80 D-Index: 12 scientists 81 D-Index: 10 scientists 82 D-Index: 5 scientists 83 D-Index: 5 scientists 84 D-Index: 13 scientists 85 D-Index: 6 scientists 86+ D-Index: 99 scientists
30 D-Index 86+

This scientist: 59 D-Index — 84th percentile

84% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 86 D-Index or more.

Research.com Recognitions

  • 2015 - ACM Gordon Bell Prize An Extreme-Scale Implicit Solver for Complex PDEs: Highly Heterogeneous Flow in Earth's Mantle
  • 2014 - SIAM Fellow For contributions to optimization of systems governed by partial differential equations and leadership to promote computational science and engineering.

Overview

Omar Ghattas is affiliated with The University of Texas at Austin in the United States. Their research focuses primarily on computer science and engineering, with significant contributions in areas such as artificial intelligence, statistical and nonlinear physics, and computational mechanics.

The scientist has made substantial contributions to several specialized subfields, including:

  • Artificial Intelligence
  • Statistical and Nonlinear Physics
  • Statistics, Probability and Uncertainty
  • Computational Mechanics
  • Materials Chemistry

Ghattas's main research topics cover a range of advanced computational and mathematical disciplines, notably:

  • Model Reduction and Neural Networks
  • Probabilistic and Robust Engineering Design
  • Gaussian Processes and Bayesian Inference
  • Advanced Multi-Objective Optimization Algorithms
  • Seismic Imaging and Inversion Techniques
  • Block Copolymer Self-Assembly
  • Markov Chains and Monte Carlo Methods

The scientist has published extensively in various research venues, with a frequent presence in:

  • arXiv (Cornell University)
  • SIAM Journal on Scientific Computing
  • Computer Methods in Applied Mechanics and Engineering
  • Journal of Computational Physics
  • Computers & Mathematics with Applications

Omar Ghattas has collaborated regularly with several coauthors, including:

  • Thomas O'Leary-Roseberry
  • Peng Chen
  • Umberto Villa
  • Lianghao Cao
  • Karen Willcox

Selected recent publications illustrate the thematic scope and research depth:

  • Frontera: The Evolution of Leadership Computing at the National Science Foundation, 2020, Practice and Experience in Advanced Research Computing
  • Learning physics-based models from data: perspectives from inverse problems and model reduction, 2021, Acta Numerica
  • Derivative-informed projected neural networks for high-dimensional parametric maps governed by PDEs, 2021, Computer Methods in Applied Mechanics and Engineering
  • Bayesian inference of heterogeneous epidemic models: Application to COVID-19 spread accounting for long-term care facilities, 2021, Computer Methods in Applied Mechanics and Engineering
  • Learning high-dimensional parametric maps via reduced basis adaptive residual networks, 2022, Computer Methods in Applied Mechanics and Engineering

The scientist's research achievements have been recognized through awards including the ACM Gordon Bell Prize in 2015 for work on an extreme-scale implicit solver addressing complex partial differential equations related to Earth's mantle flow.

In 2014, Omar Ghattas was named a SIAM Fellow for contributions to optimization of systems governed by partial differential equations and leadership to promote computational science and engineering.

Best Publications

  • p4est : Scalable Algorithms for Parallel Adaptive Mesh Refinement on Forests of Octrees

    Carsten Burstedde;Lucas C. Wilcox;Omar Ghattas

  • A Stochastic Newton MCMC Method for Large-Scale Statistical Inverse Problems with Application to Seismic Inversion

    James Martin;Lucas C. Wilcox;Carsten Burstedde;Omar Ghattas

  • Model Reduction for Large-Scale Systems with High-Dimensional Parametric Input Space

    T. Bui-Thanh;K. Willcox;O. Ghattas

  • Large-scale simulation of elastic wave propagation in heterogeneous media on parallel computers

    Hesheng Bao;Jacobo Bielak;Omar Nabih Ghattas;Loukas F Kallivokas

  • A Computational Framework for Infinite-Dimensional Bayesian Inverse Problems Part I: The Linearized Case, with Application to Global Seismic Inversion

    Tan Bui-Thanh;Omar Ghattas;James Martin;Georg Stadler

  • Parallel Lagrange--Newton--Krylov--Schur Methods for PDE-Constrained Optimization. Part I: The Krylov--Schur Solver

    George Biros;Omar Ghattas

  • The Dynamics of Plate Tectonics and Mantle Flow: From Local to Global Scales

    Georg Stadler;Michael Gurnis;Carsten Burstedde;Lucas C. Wilcox

  • Non-linear model reduction for uncertainty quantification in large-scale inverse problems

    D. Galbally;K. Fidkowski;K. Willcox;Omar Nabih Ghattas

  • A high-order discontinuous Galerkin method for wave propagation through coupled elastic-acoustic media

    Lucas C. Wilcox;Georg Stadler;Carsten Burstedde;Omar Ghattas

  • Parameter and State Model Reduction for Large-Scale Statistical Inverse Problems

    Chad Lieberman;Karen Willcox;Omar Ghattas

  • A Computational Framework for Infinite-Dimensional Bayesian Inverse Problems, Part II: Stochastic Newton MCMC with Application to Ice Sheet Flow Inverse Problems

    Noemi Petra;James Martin;Georg Stadler;Omar Ghattas

  • A Newton-CG method for large-scale three-dimensional elastic full-waveform seismic inversion

    I. Epanomeritakis;V. Akçelik;Omar Nabih Ghattas;J. Bielak

  • High Resolution Forward And Inverse Earthquake Modeling on Terascale Computers

    Vokan Akcelik;Jacobo Bielak;George Biros;Ioannis Epanomeritakis

  • Large-Scale PDE-Constrained Optimization: An Introduction

    Lorenz T. Biegler;Omar Ghattas;Matthias Heinkenschloss;Bart van Bloemen Waanders

  • From mesh generation to scientific visualization: an end-to-end approach to parallel supercomputing

    Tiankai Tu;Hongfeng Yu;Leonardo Ramirez-Guzman;Jacobo Bielak

  • Goal-oriented, model-constrained optimization for reduction of large-scale systems

    T. Bui-Thanh;K. Willcox;O. Ghattas;B. van Bloemen Waanders

  • Fast Algorithms for Bayesian Uncertainty Quantification in Large-Scale Linear Inverse Problems Based on Low-Rank Partial Hessian Approximations

    H. P. Flath;L. C. Wilcox;V. Akçelik;J. Hill

  • Parametric reduced-order models for probabilistic analysis of unsteady aerodynamic applications

    Tan Bui-Thanh;Karen Willcox;Omar Ghattas

  • Large-Scale Inverse Problems and Quantification of Uncertainty

    Lorenz Biegler;George Biros;Omar Nabih Ghattas;Matthias Heinkenschloss

  • Parallel Multiscale Gauss-Newton-Krylov Methods for Inverse Wave Propagation

    Volkan Akcelik;George Biros;Omar Ghattas

  • Parallel Lagrange--Newton--Krylov--Schur Methods for PDE-Constrained Optimization. Part II: The Lagrange--Newton Solver and Its Application to Optimal Control of Steady Viscous Flows

    George Biros;Omar Ghattas

Frequent Co-Authors

Michael Gurnis
Michael Gurnis California Institute of Technology
George Biros
George Biros The University of Texas at Austin
Karen Willcox
Karen Willcox The University of Texas at Austin
David R. O'Hallaron
David R. O'Hallaron Carnegie Mellon University
David E. Keyes
David E. Keyes King Abdullah University of Science and Technology
Jacobo Bielak
Jacobo Bielak Carnegie Mellon University
James F. Antaki
James F. Antaki Cornell University
Shijie Zhong
Shijie Zhong University of Colorado Boulder
Lorenz T. Biegler
Lorenz T. Biegler Carnegie Mellon University

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