World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
44
Citations
11612
World Ranking
5695
National Ranking
1589

Igor Mordatch publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Igor Mordatch sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 83 publications — 4th percentile

4% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Igor Mordatch D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Igor Mordatch sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 44 D-Index — 42nd percentile

42% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

Igor Mordatch is a researcher affiliated with Google in the United States, active primarily in the field of computer science. Their work spans several subfields including artificial intelligence, computer vision and pattern recognition, control and systems engineering, structural biology, and surfaces, coatings and films. The primary concentration of their research lies in artificial intelligence.

Their main topics of investigation cover a range of areas including reinforcement learning in robotics, topic modeling, multimodal machine learning applications, natural language processing techniques, robot manipulation and learning, domain adaptation and few-shot learning, as well as advanced electron microscopy techniques and applications.

Mordatch has contributed a significant number of publications, with 48 appearing in arXiv (Cornell University), along with several papers in venues such as Microscopy and Microanalysis, Proceedings of the AAAI Conference on Artificial Intelligence, Lirias (KU Leuven), and Advanced Materials Interfaces.

Some notable recent papers include:

  • Decision Transformer: Reinforcement Learning via Sequence Modeling (2021, arXiv (Cornell University))
  • PaLM-E: An Embodied Multimodal Language Model (2023, arXiv (Cornell University))
  • RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control (2023, arXiv (Cornell University))
  • Inner Monologue: Embodied Reasoning through Planning with Language Models (2022, arXiv (Cornell University))
  • Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents (2022, arXiv (Cornell University))

Frequent collaborators include Pieter Abbeel, Sergey Levine, Yilun Du, Ekin D. Cubuk, and Brian Ichter, reflecting ongoing partnerships within these areas of study.

Best Publications

  • Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

    Ryan Lowe;Yi Wu;Aviv Tamar;Jean Harb

  • Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

    Ryan Lowe;Yi Wu;Aviv Tamar;Jean Harb

  • PaLM-E: An Embodied Multimodal Language Model

    Unknown

  • Emergence of Grounded Compositional Language in Multi-Agent Populations

    Igor Mordatch;Pieter Abbeel

  • Inner Monologue: Embodied Reasoning through Planning with Language Models

    Unknown

  • Discovery of complex behaviors through contact-invariant optimization

    Igor Mordatch;Emanuel Todorov;Zoran Popović

  • Decision Transformer: Reinforcement Learning via Sequence Modeling

    Lili Chen;Kevin Lu;Aravind Rajeswaran;Kimin Lee

  • RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control

    Unknown

  • Emergent Tool Use From Multi-Agent Autocurricula

    Bowen Baker;Ingmar Kanitscheider;Todor Markov;Yi Wu

  • Learning with Opponent-Learning Awareness

    Jakob Foerster;Richard Y. Chen;Maruan Al-Shedivat;Shimon Whiteson

  • RT-1: Robotics Transformer for Real-World Control at Scale

    Unknown

  • Feature-based locomotion controllers

    Martin de Lasa;Igor Mordatch;Aaron Hertzmann

  • Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments

    Maruan Al-Shedivat;Trapit Bansal;Yuri Burda;Ilya Sutskever

  • Transfer from Simulation to Real World through Learning Deep Inverse Dynamics Model

    Paul F. Christiano;Zain Shah;Igor Mordatch;Jonas Schneider

  • Robust physics-based locomotion using low-dimensional planning

    Igor Mordatch;Martin de Lasa;Aaron Hertzmann

  • Navigation system for a 3d virtual scene

    George Fitzmaurice;Justin Matejka;Igor Mordatch;Gord Kurtenbach

  • Emergent Complexity via Multi-Agent Competition

    Trapit Bansal;Jakub Pachocki;Szymon Sidor;Ilya Sutskever

  • Implicit Generation and Generalization in Energy-Based Models.

    Yilun Du;Igor Mordatch

  • Implicit Generation and Modeling with Energy Based Models

    Yilun Du;Igor Mordatch

  • Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control

    Kendall Lowrey;Aravind Rajeswaran;Sham M. Kakade;Emanuel Todorov

  • Ensemble-CIO: Full-body dynamic motion planning that transfers to physical humanoids

    Igor Mordatch;Kendall Lowrey;Emanuel Todorov

  • Contact-invariant optimization for hand manipulation

    Igor Mordatch;Zoran Popović;Emanuel Todorov

  • Combining the benefits of function approximation and trajectory optimization.

    Igor Mordatch;Emo Todorov

  • Rearrangement: A Challenge for Embodied AI.

    Dhruv Batra;Angel X. Chang;Sonia Chernova;Andrew J. Davison

  • Interactive control of diverse complex characters with neural networks

    Igor Mordatch;Kendall Lowrey;Galen Andrew;Zoran Popovic

  • Multiscale 3D navigation

    James McCrae;Igor Mordatch;Michael Glueck;Azam Khan

  • Three-dimensional orientation indicator and controller

    Anirban Ghosh;Igor Mordatch;Azam Khan;George William Fitzmaurice

  • Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines

    Cathy Wu;Aravind Rajeswaran;Yan Duan;Vikash Kumar

  • Stylizing animation by example

    Pierre Bénard;Forrester Cole;Michael Kass;Igor Mordatch

Frequent Co-Authors

Pieter Abbeel
Pieter Abbeel University of California, Berkeley
Emanuel Todorov
Emanuel Todorov University of Washington
Azam Khan
Azam Khan Trax.co
Sergey Levine
Sergey Levine University of California, Berkeley
Vikash Kumar
Vikash Kumar University of Washington
George Fitzmaurice
George Fitzmaurice Autodesk (United States)
Aviv Tamar
Aviv Tamar Technion – Israel Institute of Technology

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