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Computer Science
UK
2025

D-Index & Metrics

Computer Science

D-Index
84
Citations
185293
World Ranking
815
National Ranking
39

David Silver 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 David Silver 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: 181 publications — 39th percentile

39% of scientists in this discipline score the same or lower.

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

David Silver 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 David Silver 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: 84 D-Index — 94th percentile

94% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2025 - Research.com Computer Science in United Kingdom Leader Award
  • 2022 - Research.com Computer Science in United Kingdom Leader Award
  • 2019 - ACM Prize in Computing For breakthrough advances in computer game-playing

Overview

David Silver is a researcher affiliated with DeepMind in the United Kingdom, with a primary focus on computer science and artificial intelligence. Their publication record includes significant contributions across several subfields, notably artificial intelligence, molecular biology, and computational theory and mathematics.

The main topics covered in David Silver's work include reinforcement learning in robotics, evolutionary algorithms and applications, artificial intelligence in games, adversarial robustness in machine learning, protein structure and dynamics, and enzyme structure and function.

Frequent publication venues for their research are:

  • arXiv (Cornell University)
  • Nature
  • The Journal of Urology
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Zenodo (CERN European Organization for Nuclear Research)

David Silver has collaborated extensively with several researchers, including:

  • Demis Hassabis
  • Satinder Singh
  • Hado van Hasselt
  • Matteo Hessel
  • André Barreto

Their recent publications include:

  • Highly accurate protein structure prediction with AlphaFold (2021, Nature)
  • Improved protein structure prediction using potentials from deep learning (2020, Nature)
  • Deep learning, reinforcement learning, and world models (2022, Neural Networks)
  • Discovering faster matrix multiplication algorithms with reinforcement learning (2022, Nature)
  • Applying and improving AlphaFold at CASP14 (2021, Proteins Structure Function and Bioinformatics)

In 2019, David Silver received the ACM Prize in Computing for breakthrough advances in computer game-playing.

Best Publications

  • Highly accurate protein structure prediction with AlphaFold

    John M. Jumper;Richard O. Evans;Alexander Pritzel;Tim Green

  • Human-level control through deep reinforcement learning

    Volodymyr Mnih;Koray Kavukcuoglu;David Silver;Andrei A. Rusu

  • 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

  • Playing Atari with Deep Reinforcement Learning

    Volodymyr Mnih;Koray Kavukcuoglu;David Silver;Alex Graves

  • Asynchronous methods for deep reinforcement learning

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

  • Deep reinforcement learning with double Q-Learning

    Hado van Hasselt;Arthur Guez;David Silver

  • 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

  • Improved protein structure prediction using potentials from deep learning

    Andrew W. Senior;Richard Evans;John Jumper;James Kirkpatrick

  • Deep reinforcement learning with double Q-learning

    H Van Hasselt;A Guez;D Silver

  • Prioritized Experience Replay

    Tom Schaul;John Quan;Ioannis Antonoglou;David Silver

  • Deterministic Policy Gradient Algorithms

    David Silver;Guy Lever;Nicolas Heess;Thomas Degris

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

    Julian Schrittwieser;Ioannis Antonoglou;Thomas Hubert;Karen Simonyan

  • Rainbow: Combining Improvements in Deep Reinforcement Learning

    Matteo Hessel;Joseph Modayil;Hado van Hasselt;Tom Schaul

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

    David Silver;Thomas Hubert;Julian Schrittwieser;Ioannis Antonoglou

  • Monte-Carlo Planning in Large POMDPs

    David Silver;Joel Veness

  • Asynchronous Methods for Deep Reinforcement Learning

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

  • Reinforcement Learning with Unsupervised Auxiliary Tasks

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

  • Emergence of Locomotion Behaviours in Rich Environments

    Nicolas Heess;Dhruva Tb;Srinivasan Sriram;Jay Lemmon

Frequent Co-Authors

Tom Schaul
Tom Schaul DeepMind (United Kingdom)
Hado van Hasselt
Hado van Hasselt University College London
Nicolas Heess
Nicolas Heess DeepMind (United Kingdom)
Koray Kavukcuoglu
Koray Kavukcuoglu DeepMind (United Kingdom)
Timothy P. Lillicrap
Timothy P. Lillicrap University College London
Demis Hassabis
Demis Hassabis Google (United States)
Thore Graepel
Thore Graepel University College London
Volodymyr Mnih
Volodymyr Mnih DeepMind (United Kingdom)
Rémi Munos
Rémi Munos French Institute for Research in Computer Science and Automation - INRIA

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