World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
64
Citations
16571
World Ranking
2600
National Ranking
151

Shimon Whiteson 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 Shimon Whiteson 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: 297 publications — 73rd percentile

73% of scientists in this discipline score the same or lower.

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

Shimon Whiteson 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 Shimon Whiteson 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: 64 D-Index — 82nd percentile

82% of scientists in this discipline score the same or lower.

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

Overview

Shimon Whiteson is affiliated with the University of Oxford in the United Kingdom and has contributed extensively to the field of computer science, with a primary focus on artificial intelligence and reinforcement learning. Their research encompasses a wide range of topics, including reinforcement learning in robotics, adversarial robustness in machine learning, autonomous vehicle technology and safety, advanced bandit algorithms research, model reduction and neural networks, data stream mining techniques, and machine learning and data classification.

The scientist's publication record includes numerous papers primarily published on the arXiv platform managed by Cornell University. Among the recent papers are:

  • Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning, 2020, arXiv (Cornell University)
  • Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?, 2020, arXiv (Cornell University)
  • Weighted QMIX: Expanding monotonic value function factorisation for deep multi-agent reinforcement learning, 2020, arXiv (Cornell University)
  • FACMAC: Factored Multi-Agent Centralised Policy Gradients, 2020, arXiv (Cornell University)
  • RODE: Learning Roles to Decompose Multi-Agent Tasks, 2020, arXiv (Cornell University)

Shimon Whiteson frequently collaborates with a set of co-authors who have contributed to multiple publications, which include Jakob Foerster, Maximilian Igl, Anuj Mahajan, Risto Vuorio, and Shangtong Zhang. Their combined work has focused largely on advancing multi-agent reinforcement learning methods and their applications.

Frequent publication venues for this scientist reflect a concentration in open-access and specialized conferences, including:

  • arXiv (Cornell University)
  • Autonomous Agents and Multi-Agent Systems
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
  • 2022 International Conference on Robotics and Automation (ICRA)

Their research spans core subfields of computer science such as artificial intelligence, automotive engineering, computational theory and mathematics, management science and operations research, and cognitive neuroscience. The work of Shimon Whiteson contributes to understanding and developing computational techniques applicable to real-world problems involving autonomous systems, robotics, and robust machine learning frameworks.

Best Publications

  • Counterfactual Multi-Agent Policy Gradients

    Jakob Foerster;Gregory Farquhar;Triantafyllos Afouras;Nantas Nardelli

  • Learning to Communicate with Deep Multi-Agent Reinforcement Learning

    Jakob N. Foerster;Yannis M. Assael;Nando de Freitas;Shimon Whiteson

  • Learning to Communicate with Deep Multi−Agent Reinforcement Learning

    Jakob Foerster;Ioannis Alexandros Assael;Nando de Freitas;Shimon Whiteson

  • Counterfactual Multi−Agent Policy Gradients

    Jakob N. Foerster;Gregory Farquhar;Triantafyllos Afouras;Nantas Nardelli

  • A survey of multi-objective sequential decision-making

    Diederik M. Roijers;Peter Vamplew;Shimon Whiteson;Richard Dazeley

  • QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

    Tabish Rashid;Mikayel Samvelyan;Christian Schroeder;Gregory Farquhar

  • Stabilising experience replay for deep multi-agent reinforcement learning

    Jakob Foerster;Nantas Nardelli;Gregory Farquhar;Triantafyllos Afouras

  • QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

    Tabish Rashid;Mikayel Samvelyan;Christian Schroeder de Witt;Gregory Farquhar

  • Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

    Tabish Rashid;Mikayel Samvelyan;Christian Schröder de Witt;Gregory Farquhar

  • Evolutionary Function Approximation for Reinforcement Learning

    Shimon Whiteson;Peter Stone

  • Learning with Opponent-Learning Awareness

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

  • Multiagent Reinforcement Learning for Urban Traffic Control Using Coordination Graphs

    Lior Kuyer;Shimon Whiteson;Bram Bakker;Nikos Vlassis

  • LipNet: End-to-End Sentence-level Lipreading

    Yannis M. Assael;Brendan Shillingford;Shimon Whiteson;Nando de Freitas

  • A theoretical and empirical analysis of Expected Sarsa

    Harm van Seijen;Hado van Hasselt;Shimon Whiteson;Marco Wiering

  • Fast Context Adaptation via Meta-Learning

    Luisa Zintgraf;Kyriacos Shiarli;Vitaly Kurin;Katja Hofmann

  • The StarCraft Multi-Agent Challenge

    Mikayel Samvelyan;Tabish Rashid;Christian Schroeder de Witt;Gregory Farquhar

  • A Survey of Reinforcement Learning Informed by Natural Language

    Jelena Luketina;Nantas Nardelli;Nantas Nardelli;Gregory Farquhar;Gregory Farquhar;Jakob N. Foerster

  • Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?

    Christian Schroeder de Witt;Tarun Gupta;Denys Makoviichuk;Viktor Makoviychuk

  • Deep Variational Reinforcement Learning for POMDPs

    Maximilian Igl;Luisa M. Zintgraf;Tuan Anh Le;Frank Wood

  • LipNet: Sentence-level Lipreading.

    Yannis M. Assael;Brendan Shillingford;Shimon Whiteson;Nando de Freitas

  • VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning

    Luisa Zintgraf;Kyriacos Shiarlis;Maximilian Igl;Sebastian Schulze

  • MAVEN: Multi−Agent Variational Exploration

    Anuj Mahajan;Tabish Rashid;Mikayel Samvelyan;Shimon Whiteson

Frequent Co-Authors

Jakob Foerster
Jakob Foerster University of Oxford
Maarten de Rijke
Maarten de Rijke University of Amsterdam
Peter Stone
Peter Stone The University of Texas at Austin
Philip H. S. Torr
Philip H. S. Torr University of Oxford
Tim Rocktäschel
Tim Rocktäschel University College London
Nando de Freitas
Nando de Freitas DeepMind (United Kingdom)
Edward Grefenstette
Edward Grefenstette University College London
Risto Miikkulainen
Risto Miikkulainen The University of Texas at Austin
Eric P. Xing
Eric P. Xing Mohamed bin Zayed University of Artificial Intelligence
Rémi Munos
Rémi Munos French Institute for Research in Computer Science and Automation - INRIA

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