World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
78
Citations
36772
World Ranking
1169
National Ranking
71

Satinder Singh 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 Satinder Singh 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: 344 publications — 81st percentile

81% of scientists in this discipline score the same or lower.

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

Satinder Singh 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 Satinder Singh 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: 78 D-Index — 92nd percentile

92% 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

  • 2012 - Fellow of Alfred P. Sloan Foundation

Overview

Satinder Singh is affiliated with DeepMind in the United Kingdom. Their research spans the intersections of computer science and medicine, with a focus on artificial intelligence and related subfields. The main areas of study include artificial intelligence, computer vision and pattern recognition, genetics, oncology, and surgery.

The scientist's work covers several topics, including reinforcement learning in robotics, domain adaptation and few-shot learning, adversarial robustness in machine learning, COVID-19 and healthcare impacts, nasal surgery and airway studies, evolutionary algorithms and applications, and cleft lip and palate research.

Among their recent papers are:

  • Reward is enough, 2021, published in Artificial Intelligence
  • Discovering Reinforcement Learning Algorithms, 2020, published on arXiv (Cornell University)
  • Meta-Gradient Reinforcement Learning with an Objective Discovered Online, 2020, published on arXiv (Cornell University)
  • A Self-Tuning Actor-Critic Algorithm, 2020, published on arXiv (Cornell University)
  • Comparative Assessment of Autogenous Cancellous Bone Graft and Bovine-Derived Demineralized Bone Matrix for Secondary Alveolar Bone Grafting in Patients With Unilateral Cleft Lip and Palate, 2021, published in The Cleft Palate-Craniofacial Journal

Frequent coauthors include:

  • Tom Zahavy
  • David Silver
  • Hado van Hasselt
  • Sebastian Flennerhag
  • Vidya Rattan

The scientist commonly publishes in venues such as arXiv (Cornell University), Indian Journal of Otolaryngology and Head & Neck Surgery, Journal of Maxillofacial and Oral Surgery, Journal of Oral Biology and Craniofacial Research, and Scholarly Journal of Otolaryngology.

Satinder Singh received recognition as a Fellow of the Alfred P. Sloan Foundation in 2012.

Best Publications

  • Policy Gradient Methods for Reinforcement Learning with Function Approximation

    Richard S Sutton;David A. McAllester;Satinder P. Singh;Yishay Mansour

  • Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning

    Richard S. Sutton;Doina Precup;Satinder Singh

  • Learning to act using real-time dynamic programming

    Andrew G. Barto;Steven J. Bradtke;Satinder P. Singh

  • Near-Optimal Reinforcement Learning in Polynomial Time

    Michael Kearns;Satinder Singh

  • Convergence of Stochastic Iterative Dynamic Programming Algorithms

    Tommi Jaakkola;Michael I. Jordan;Satinder P. Singh

  • Reinforcement learning with replacing eligibility traces

    Satinder P. Singh;Richard S. Sutton

  • Convergence Results for Single-Step On-PolicyReinforcement-Learning Algorithms

    Satinder Singh;Tommi Jaakkola;Michael L. Littman;Csaba Szepesvári

  • Intrinsically Motivated Reinforcement Learning

    Nuttapong Chentanez;Andrew G. Barto;Satinder P. Singh

  • Action-conditional video prediction using deep networks in Atari games

    Junhyuk Oh;Xiaoxiao Guo;Honglak Lee;Richard Lewis

  • Eligibility Traces for Off-Policy Policy Evaluation

    Doina Precup;Richard S. Sutton;Satinder P. Singh

  • Graphical models for game theory

    Michael J. Kearns;Michael L. Littman;Satinder P. Singh

  • Learning Without State-Estimation in Partially Observable Markovian Decision Processes

    Satinder P. Singh;Tommi S. Jaakkola;Michael I. Jordan

  • Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems

    Tommi Jaakkola;Satinder P. Singh;Michael I. Jordan

  • Intrinsically Motivated Learning of Hierarchical Collections of Skills

    Andrew G. Barto;Satinder Singh;Nuttapong Chentanez

  • Intrinsically Motivated Reinforcement Learning: An Evolutionary Perspective

    Satinder Singh;Richard L Lewis;Andrew G Barto;Jonathan Sorg

  • Transfer of Learning by Composing Solutions of Elemental Sequential Tasks

    Satinder Pal Singh

  • Optimizing dialogue management with reinforcement learning: experiments with the NJFun system

    Satinder Singh;Diane Litman;Michael Kearns;Marilyn Walker

  • Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning

    Xiaoxiao Guo;Satinder Singh;Honglak Lee;Richard L Lewis

  • Reinforcement Learning with Soft State Aggregation

    Satinder P. Singh;Tommi Jaakkola;Michael I. Jordan

  • Reward is enough

    David Silver;Satinder P. Singh;Doina Precup;Richard S. Sutton

  • Near-Optimal Reinforcement Learning in Polynominal Time

    Michael J. Kearns;Satinder P. Singh

Frequent Co-Authors

Richard L. Lewis
Richard L. Lewis University of Michigan–Ann Arbor
Edmund H. Durfee
Edmund H. Durfee University of Michigan–Ann Arbor
Michael Kearns
Michael Kearns University of Pennsylvania
Michael P. Wellman
Michael P. Wellman University of Michigan–Ann Arbor
Honglak Lee
Honglak Lee University of Michigan–Ann Arbor
Yevgeniy Vorobeychik
Yevgeniy Vorobeychik Washington University in St. Louis
Andrew G. Barto
Andrew G. Barto University of Massachusetts Amherst
Doina Precup
Doina Precup McGill University
Michael L. Littman
Michael L. Littman Brown University
David Silver
David Silver DeepMind (United Kingdom)

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