World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
38
Citations
9512
World Ranking
10007
National Ranking
623

Joel Z. Leibo 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 Joel Z. Leibo 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: 129 publications — 18th percentile

18% of scientists in this discipline score the same or lower.

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

Joel Z. Leibo 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 Joel Z. Leibo 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: 38 D-Index — 30th percentile

30% of scientists in this discipline score the same or lower.

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

Overview

Joel Z. Leibo is affiliated with DeepMind in the United Kingdom and has a significant body of research focused on social sciences, particularly in the intersection of artificial intelligence and behavioral studies. Their work spans multiple subfields including sociology and political science, artificial intelligence, safety research, management science and operations research, and cognitive neuroscience.

Leibo's research contributions are concentrated in areas such as evolutionary game theory and cooperation, experimental behavioral economics studies, reinforcement learning in robotics, language and cultural evolution, game theory and applications, evolutionary psychology and human behavior, and auction theory and applications.

Frequent co-authors collaborating with Leibo include Edgar A. Duéñez-Guzmán, Edward Hughes, Alexander Sasha Vezhnevets, Kevin R. McKee, and Raphaël Koster. These collaborations reflect a multidisciplinary approach integrating different perspectives within the social and computational sciences.

Leibo has published extensively in prominent venues, with a notable presence in arXiv (Cornell University), where 42 publications are recorded. Other publication venues include the Proceedings of the National Academy of Sciences, Behavioral and Brain Sciences, Autonomous Agents and Multi-Agent Systems, and Neuron.

Selected recent papers illustrate the breadth of their research topics and include:

  • Learning a Generic Value-Selection Heuristic Inside a Constraint Programming Solver, 2023, arXiv (Cornell University)
  • Promises and challenges of human computational ethology, 2021, Neuron
  • Machine culture, 2023, Nature Human Behaviour
  • Rethink reporting of evaluation results in AI, 2023, Science
  • Negotiating team formation using deep reinforcement learning, 2020, Artificial Intelligence

Best Publications

  • Reinforcement Learning with Unsupervised Auxiliary Tasks

    Max Jaderberg;Volodymyr Mnih;Wojciech Marian Czarnecki;Tom Schaul

  • Prefrontal cortex as a meta-reinforcement learning system

    Jane X. Wang;Zeb Kurth-Nelson;Dharshan Kumaran;Dhruva Tirumala

  • Learning to reinforcement learn

    Jane X. Wang;Zeb Kurth-Nelson;Dhruva Tirumala;Hubert Soyer

  • Deep Q-learning from Demonstrations

    Todd Hester;Matej Vecerik;Olivier Pietquin;Marc Lanctot

  • Deep Q-learning From Demonstrations.

    Todd Hester;Matej Vecerík;Olivier Pietquin;Marc Lanctot

  • Human-level performance in first-person multiplayer games with population-based deep reinforcement learning.

    Max Jaderberg;Wojciech M. Czarnecki;Iain Dunning;Luke Marris

  • Learning to reinforcement learn

    Jane X Wang;Zeb Kurth-Nelson;Dhruva Tirumala;Hubert Soyer

  • Value-Decomposition Networks For Cooperative Multi-Agent Learning

    Peter Sunehag;Guy Lever;Audrunas Gruslys;Wojciech Marian Czarnecki

  • Multi-agent Reinforcement Learning in Sequential Social Dilemmas

    Joel Z. Leibo;Vinicius Zambaldi;Marc Lanctot;Janusz Marecki

  • Human-level performance in 3D multiplayer games with population-based reinforcement learning

    Max Jaderberg;Wojciech M. Czarnecki;Iain Dunning;Luke Marris

  • Value-Decomposition Networks For Cooperative Multi-Agent Learning Based On Team Reward

    Peter Sunehag;Guy Lever;Audrunas Gruslys;Wojciech Marian Czarnecki

  • The dynamics of invariant object recognition in the human visual system.

    Leyla Isik;Ethan M. Meyers;Ethan M. Meyers;Joel Z. Leibo;Joel Z. Leibo;Tomaso A. Poggio;Tomaso A. Poggio

  • Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning

    Natasha Jaques;Angeliki Lazaridou;Edward Hughes;Çaglar Gülçehre

  • Multi-agent Reinforcement Learning in Sequential Social Dilemmas

    Joel Z. Leibo;Vinicius Zambaldi;Marc Lanctot;Janusz Marecki

  • Unsupervised Predictive Memory in a Goal-Directed Agent

    Greg Wayne;Chia-Chun Hung;David Amos;Mehdi Mirza

  • Model-Free Episodic Control

    Charles Blundell;Benigno Uria;Alexander Pritzel;Yazhe Li

  • Using Fast Weights to Attend to the Recent Past

    Jimmy Ba;Geoffrey E. Hinton;Volodymyr Mnih;Joel Z. Leibo

  • Inequity aversion improves cooperation in intertemporal social dilemmas

    Edward Hughes;Joel Z. Leibo;Matthew G. Phillips;Karl Tuyls

  • Learning from Demonstrations for Real World Reinforcement Learning

    Todd Hester;Matej Vecerík;Olivier Pietquin;Marc Lanctot

  • How important is weight symmetry in backpropagation

    Qianli Liao;Joel Z. Leibo;Tomaso Poggio

  • Unsupervised learning of invariant representations

    Fabio Anselmi;Joel Z. Leibo;Lorenzo Rosasco;Jim Mutch

  • A multi-agent reinforcement learning model of common-pool resource appropriation

    Julien Pérolat;Joel Z. Leibo;Vinícius Flores Zambaldi;Charles Beattie

  • Emergent Communication through Negotiation

    Kris Cao;Angeliki Lazaridou;Marc Lanctot;Joel Z. Leibo

  • Unsupervised Learning of Invariant Representations in Hierarchical Architectures

    Fabio Anselmi;Joel Z. Leibo;Lorenzo Rosasco;Jim Mutch

  • Open Problems in Cooperative AI

    Allan Dafoe;Edward Hughes;Yoram Bachrach;Tantum Collins

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