World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
64
Citations
86663
World Ranking
2504
National Ranking
142

Timothy P. Lillicrap 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 Timothy P. Lillicrap 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: 250 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: 560 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: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 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.

Timothy P. Lillicrap 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 Timothy P. Lillicrap sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 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

Timothy P. Lillicrap is affiliated with University College London in the United Kingdom and specializes primarily in the field of Computer Science. They have contributed extensively to research areas including Artificial Intelligence, Cognitive Neuroscience, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, and Control and Systems Engineering.

Their research predominantly focuses on key topics such as Reinforcement Learning in Robotics, Neural dynamics and brain function, Multimodal Machine Learning Applications, Advanced Memory and Neural Computing, Robot Manipulation and Learning, Topic Modeling, and Natural Language Processing Techniques.

Timothy Lillicrap has published in multiple venues, with a noticeable presence in both preprint and peer-reviewed outlets. The frequent publication venues include arXiv (Cornell University), bioRxiv (Cold Spring Harbor Laboratory), Nature reviews. Neuroscience, Nature Communications, and Software Impacts.

  • Backpropagation and the brain, 2020, Nature reviews. Neuroscience
  • Gemini: A Family of Highly Capable Multimodal Models, 2023, arXiv (Cornell University)
  • Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context, 2024, arXiv (Cornell University)
  • Catalyzing next-generation Artificial Intelligence through NeuroAI, 2023, Nature Communications
  • dm_control: Software and tasks for continuous control, 2020, Software Impacts

Their collaborations include frequent co-authorship with a team of researchers who have contributed to multiple publications together. Notable frequent co-authors are Adam Santoro, Alistair Muldal, Blake A. Richards, Petko Georgiev, and Peter C. Humphreys.

Best Publications

  • Mastering the game of Go with deep neural networks and tree search

    David Silver;Aja Huang;Christopher J. Maddison;Arthur Guez

  • Continuous control with deep reinforcement learning

    Timothy P. Lillicrap;Jonathan J. Hunt;Alexander Pritzel;Nicolas Heess

  • Mastering the game of Go without human knowledge

    David Silver;Julian Schrittwieser;Karen Simonyan;Ioannis Antonoglou

  • Asynchronous methods for deep reinforcement learning

    Volodymyr Mnih;Adrià Puigdomènech Badia;Mehdi Mirza;Alex Graves

  • Matching networks for one shot learning

    Oriol Vinyals;Charles Blundell;Timothy Lillicrap;Koray Kavukcuoglu

  • A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play.

    David Silver;Thomas Hubert;Julian Schrittwieser;Ioannis Antonoglou

  • Grandmaster level in StarCraft II using multi-agent reinforcement learning.

    Oriol Vinyals;Igor Babuschkin;Wojciech M. Czarnecki;Michaël Mathieu

  • Mastering Atari, Go, chess and shogi by planning with a learned model

    Julian Schrittwieser;Ioannis Antonoglou;Thomas Hubert;Karen Simonyan

  • Meta-learning with memory-augmented neural networks

    Adam Santoro;Sergey Bartunov;Matthew Botvinick;Daan Wierstra

  • Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates

    Shixiang Gu;Ethan Holly;Timothy Lillicrap;Sergey Levine

  • A simple neural network module for relational reasoning

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

  • Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

    David Silver;Thomas Hubert;Julian Schrittwieser;Ioannis Antonoglou

  • Asynchronous Methods for Deep Reinforcement Learning

    Volodymyr Mnih;Adrià Puigdomènech Badia;Mehdi Mirza;Alex Graves

  • Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

    Unknown

  • A deep learning framework for neuroscience

    Blake A Richards;Timothy P Lillicrap;Philippe Beaudoin;Yoshua Bengio;Yoshua Bengio

  • StarCraft II: A New Challenge for Reinforcement Learning

    Oriol Vinyals;Timo Ewalds;Sergey Bartunov;Petko Georgiev

  • Continuous deep Q-learning with model-based acceleration

    Shixiang Gu;Timothy Lillicrap;Ilya Sutskever;Sergey Levine

  • Random synaptic feedback weights support error backpropagation for deep learning.

    Timothy P. Lillicrap;Daniel Cownden;Douglas B. Tweed;Colin J. Akerman

  • Backpropagation and the brain

    Timothy P. Lillicrap;Adam Santoro;Luke Marris;Colin J. Akerman

  • Vector-based navigation using grid-like representations in artificial agents

    Andrea Banino;Caswell Barry;Benigno Uria;Charles Blundell

  • DeepMind Control Suite

    Yuval Tassa;Yotam Doron;Alistair Muldal;Tom Erez

  • Dream to Control: Learning Behaviors by Latent Imagination

    Danijar Hafner;Timothy Lillicrap;Jimmy Ba;Mohammad Norouzi

  • Experience Replay for Continual Learning

    David Rolnick;Arun Ahuja;Jonathan Schwarz;Timothy P. Lillicrap

Frequent Co-Authors

Nicolas Heess
Nicolas Heess DeepMind (United Kingdom)
David Silver
David Silver DeepMind (United Kingdom)
Yuval Tassa
Yuval Tassa Google (United States)
Sergey Levine
Sergey Levine University of California, Berkeley
Shixiang Gu
Shixiang Gu Google (United States)
Matthew Botvinick
Matthew Botvinick Yale University
Demis Hassabis
Demis Hassabis Google (United States)
Razvan Pascanu
Razvan Pascanu DeepMind (United Kingdom)
Nicholas G. Hatsopoulos
Nicholas G. Hatsopoulos University of Chicago
Stephen Scott
Stephen Scott Queen's University

External Links

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