World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
49
Citations
15381
World Ranking
5766
National Ranking
347

Peter W. Battaglia publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Peter W. Battaglia sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 101 publications — 9th percentile

9% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Peter W. Battaglia D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Peter W. Battaglia sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 49 D-Index — 60th percentile

60% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Peter W. Battaglia is affiliated with DeepMind in the United Kingdom. Their research primarily spans the field of Computer Science, with a focus on Artificial Intelligence, Atmospheric Science, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, and Astronomy and Astrophysics. Within these fields, Battaglia's work encompasses advanced topics such as Advanced Graph Neural Networks, Meteorological Phenomena and Simulations, Model Reduction and Neural Networks, Machine Learning in Materials Science, Climate Variability and Models, Tropical and Extratropical Cyclones Research, and Computational Physics and Python Applications.

Recent publications by Battaglia include:

  • Understanding the Impact of Value Selection Heuristics in Scheduling Problems (2025, arXiv (Cornell University))
  • Learning skillful medium-range global weather forecasting (2023, Science)
  • Learning to Simulate Complex Physics with Graph Networks (2020, arXiv (Cornell University))
  • Advancing mathematics by guiding human intuition with AI (2021, Nature)
  • Discovering Symbolic Models from Deep Learning with Inductive Biases (2020, arXiv (Cornell University))

Frequent collaborators in Battaglia's work include Álvaro Sánchez-González, Miles Cranmer, Shirley Ho, Stephan Hoyer, and Tobias Pfaff.

Battaglia has contributed extensively to publications in venues such as:

  • arXiv (Cornell University)
  • Nature
  • Proceedings of the National Academy of Sciences
  • Nature Human Behaviour
  • Zenodo (CERN European Organization for Nuclear Research)

Best Publications

  • Relational inductive biases, deep learning, and graph networks

    Peter W. Battaglia;Jessica B. Hamrick;Victor Bapst;Alvaro Sanchez-Gonzalez

  • A simple neural network module for relational reasoning

    Adam Santoro;David Raposo;David G. T. Barrett;Mateusz Malinowski

  • Interaction networks for learning about objects, relations and physics

    Peter Battaglia;Razvan Pascanu;Matthew Lai;Danilo Jimenez Rezende

  • Simulation as an engine of physical scene understanding

    Peter W. Battaglia;Jessica B. Hamrick;Joshua B. Tenenbaum

  • Learning Deep Generative Models of Graphs

    Yujia Li;Oriol Vinyals;Chris Dyer;Razvan Pascanu

  • Bayesian integration of visual and auditory signals for spatial localization

    Peter W. Battaglia;Robert A. Jacobs;Richard N. Aslin

  • Learning to Simulate Complex Physics with Graph Networks

    Alvaro Sanchez-Gonzalez;Jonathan Godwin;Tobias Pfaff;Rex Ying

  • Advancing mathematics by guiding human intuition with AI.

    Alex Davies;Petar Veličković;Lars Buesing;Sam Blackwell

  • Graph Networks as Learnable Physics Engines for Inference and Control

    Alvaro Sanchez-Gonzalez;Nicolas Heess;Jost Tobias Springenberg;Josh Merel

  • Graph neural networks in particle physics

    Jonathan Shlomi;Peter W. Battaglia;Jean-Roch Vlimant

  • Unsupervised Learning of 3D Structure from Images

    Danilo Jimenez Rezende;S. M. Ali Eslami;Shakir Mohamed;Peter W. Battaglia

  • Discovering Symbolic Models from Deep Learning with Inductive Biases

    Miles D. Cranmer;Alvaro Sanchez-Gonzalez;Peter W. Battaglia;Rui Xu

  • Imagination-Augmented Agents for Deep Reinforcement Learning

    Sébastien Racanière;Theophane Weber;David P. Reichert;Lars Buesing

  • Mind Games: Game Engines as an Architecture for Intuitive Physics

    Tomer D. Ullman;Tomer D. Ullman;Elizabeth Spelke;Peter Battaglia;Joshua B. Tenenbaum

  • Visual Interaction Networks: Learning a Physics Simulator from Video

    Nicholas Watters;Daniel Zoran;Theophane Weber;Peter W. Battaglia

  • Imagination-Augmented Agents for Deep Reinforcement Learning

    Théophane Weber;Sébastien Racanière;David P. Reichert;Lars Buesing

  • Lagrangian Neural Networks

    Miles D. Cranmer;Sam Greydanus;Stephan Hoyer;Peter W. Battaglia

  • Relational Deep Reinforcement Learning.

    Vinícius Flores Zambaldi;David Raposo;Adam Santoro;Victor Bapst

  • ETA Prediction with Graph Neural Networks in Google Maps

    Austin Derrow-Pinion;Jennifer She;David Wong;Oliver Lange

  • Learning to Simulate Complex Physics with Graph Networks

    Alvaro Sanchez;Jonathan Godwin;Tobias Pfaff;Rex

  • Deep reinforcement learning with relational inductive biases

    Vinícius Flores Zambaldi;David Raposo;Adam Santoro;Victor Bapst

  • PolyGen: An Autoregressive Generative Model of 3D Meshes

    Charlie Nash;Yaroslav Ganin;S. M. Ali Eslami;Peter Battaglia

Frequent Co-Authors

Razvan Pascanu
Razvan Pascanu DeepMind (United Kingdom)
Nicolas Heess
Nicolas Heess DeepMind (United Kingdom)
Matthew Botvinick
Matthew Botvinick Yale University
Oriol Vinyals
Oriol Vinyals DeepMind (United Kingdom)
Paul Schrater
Paul Schrater University of Minnesota
Daniel Kersten
Daniel Kersten University of Minnesota
Timothy P. Lillicrap
Timothy P. Lillicrap University College London
Danilo Jimenez Rezende
Danilo Jimenez Rezende DeepMind (United Kingdom)
David N. Spergel
David N. Spergel Princeton University

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring computer science in the USA opens the door to many related opportunities in tech-focused education and careers. For students interested in broadening their skills, top online electrical engineering schools offer flexible programs that complement computer science and expand your technical know-how.

Many learners also seek easy certifications to get online to quickly enhance their resumes and qualify for entry-level positions with strong salaries. These certifications can lead to well-paying roles in IT, data analysis, and more, even as you continue your formal studies.

For those eager to advance their careers in a shorter time, pursuing one of the shortest master degree programs is a practical option. These accelerated degrees help you gain expertise without a long time commitment.

Considering today’s competitive landscape, focusing on most in demand masters degrees will ensure your credentials align with current employer needs. Whether you’re seeking advanced technical skills or aiming for leadership roles, these pathways can complement your studies in computer science and widen your career prospects.

Best Scientists Citing Peter W. Battaglia

Trending Scientists

Recently Published Articles