World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
48
Citations
82027
World Ranking
5986
National Ranking
361

Daan Wierstra 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 Daan Wierstra 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: 64 publications — 1st percentile

1% of scientists in this discipline score the same or lower.

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

Daan Wierstra 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 Daan Wierstra 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: 48 D-Index — 58th percentile

58% of scientists in this discipline score the same or lower.

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

Overview

Daan Wierstra is affiliated with DeepMind in the United Kingdom. Their work spans the field of computer science, with a focus on artificial intelligence, statistical and nonlinear physics, computer vision and pattern recognition, as well as computer science applications.

The scientist's research covers several main topics, including:

  • Advanced Graph Neural Networks
  • Bayesian Modeling and Causal Inference
  • Complex Network Analysis Techniques
  • Robotic Path Planning Algorithms
  • Teaching and Learning Programming

Wierstra's recent papers demonstrate engagement with problems in scheduling, embodied agents, and scalability in simulated environments. Notable publications include:

  • "Understanding the Impact of Value Selection Heuristics in Scheduling Problems," 2025, arXiv (Cornell University)
  • "Scaling Instructable Agents Across Many Simulated Worlds," 2024, arXiv (Cornell University)
  • "SIMA 2: A Generalist Embodied Agent for Virtual Worlds," 2025, arXiv (Cornell University)

Frequent co-authors in Wierstra's work include:

  • Ryan Faulkner
  • SIMA Team
  • Maria Abi Raad
  • Frederic Besse
  • Adrian Bolton

Wierstra's publications are primarily found in arXiv, a platform known for preprints and early dissemination of research findings. This venue features three of their recorded publications.

The combination of Wierstra's research topics and publication record indicates a focus on integrating machine learning techniques with complex systems, particularly in environments requiring autonomous decision-making and instruction following. Their work also touches upon programming education within the context of artificial intelligence advancements.

Best Publications

  • Human-level control through deep reinforcement learning

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

  • Continuous control with deep reinforcement learning

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

  • Playing Atari with Deep Reinforcement Learning

    Volodymyr Mnih;Koray Kavukcuoglu;David Silver;Alex Graves

  • Matching networks for one shot learning

    Oriol Vinyals;Charles Blundell;Timothy Lillicrap;Koray Kavukcuoglu

  • Stochastic Backpropagation and Approximate Inference in Deep Generative Models

    Danilo Jimenez Rezende;Shakir Mohamed;Daan Wierstra

  • Relational inductive biases, deep learning, and graph networks

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

  • Deterministic Policy Gradient Algorithms

    David Silver;Guy Lever;Nicolas Heess;Thomas Degris

  • Weight Uncertainty in Neural Network

    Charles Blundell;Julien Cornebise;Koray Kavukcuoglu;Daan Wierstra

  • DRAW: A Recurrent Neural Network For Image Generation

    Karol Gregor;Ivo Danihelka;Alex Graves;Danilo Rezende

  • Weight Uncertainty in Neural Networks

    Charles Blundell;Julien Cornebise;Koray Kavukcuoglu;Daan Wierstra

  • Meta-learning with memory-augmented neural networks

    Adam Santoro;Sergey Bartunov;Matthew Botvinick;Daan Wierstra

  • PathNet: Evolution Channels Gradient Descent in Super Neural Networks

    Chrisantha Fernando;Dylan Banarse;Charles Blundell;Yori Zwols

  • Natural Evolution Strategies

    D. Wierstra;T. Schaul;J. Peters;J. Schmidhuber

  • Natural evolution strategies

    Daan Wierstra;Tom Schaul;Tobias Glasmachers;Yi Sun

  • Neural scene representation and rendering

    S. M. Ali Eslami;Danilo Jimenez Rezende;Frederic Besse;Fabio Viola

  • One-shot Learning with Memory-Augmented Neural Networks

    Adam Santoro;Sergey Bartunov;Matthew Botvinick;Daan Wierstra

  • Training Recurrent Networks by Evolino

    Jürgen Schmidhuber;Daan Wierstra;Matteo Gagliolo;Faustino Gomez

  • Imagination-Augmented Agents for Deep Reinforcement Learning

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

  • A System for Robotic Heart Surgery that Learns to Tie Knots Using Recurrent Neural Networks

    H. Mayer;F. Gomez;D. Wierstra;I. Nagy

  • One-shot generalization in deep generative models

    Danilo J. Rezende;Shakir Mohamed;Ivo Danihelka;Karol Gregor

  • Deep AutoRegressive Networks

    Karol Gregor;Ivo Danihelka;Andriy Mnih;Charles Blundell

Frequent Co-Authors

Jürgen Schmidhuber
Jürgen Schmidhuber King Abdullah University of Science and Technology
Danilo Jimenez Rezende
Danilo Jimenez Rezende DeepMind (United Kingdom)
Tom Schaul
Tom Schaul DeepMind (United Kingdom)
Oriol Vinyals
Oriol Vinyals DeepMind (United Kingdom)
Charles Blundell
Charles Blundell DeepMind (United Kingdom)
David Silver
David Silver DeepMind (United Kingdom)
Timothy P. Lillicrap
Timothy P. Lillicrap University College London
Demis Hassabis
Demis Hassabis Google (United States)
Nicolas Heess
Nicolas Heess DeepMind (United Kingdom)
Koray Kavukcuoglu
Koray Kavukcuoglu DeepMind (United Kingdom)

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