World's Best Scientists 2026 revealed!
Ilya Sutskever

Ilya Sutskever

D-Index & Metrics

Computer Science

D-Index
68
Citations
418151
World Ranking
2012
National Ranking
1015

Ilya Sutskever 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 Ilya Sutskever 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: 113 publications — 12th percentile

12% of scientists in this discipline score the same or lower.

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

Ilya Sutskever 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 Ilya Sutskever 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: 68 D-Index — 86th percentile

86% of scientists in this discipline score the same or lower.

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

Overview

Ilya Sutskever is affiliated with OpenAI in the United States and is primarily active in the field of Computer Science. Their work has contributed substantially to several subfields, including Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Information Systems, and Computational Theory and Mathematics.

The scientist's research spans a variety of topics including:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Multimodal Machine Learning Applications
  • Speech and Audio Processing
  • Generative Adversarial Networks and Image Synthesis
  • Text Readability and Simplification
  • Speech Recognition and Synthesis

Among recent publications, the following works are notable:

  • Evaluating the Effectiveness of Large Language Models in Representing Textual Descriptions of Geometry and Spatial Relations (Short Paper), 2023, Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
  • Learning Transferable Visual Models From Natural Language Supervision, 2021, arXiv (Cornell University)
  • Language Models are Few-Shot Learners, 2020, arXiv (Cornell University)
  • Evaluating Large Language Models Trained on Code, 2021, arXiv (Cornell University)
  • Zero-Shot Text-to-Image Generation, 2021, arXiv (Cornell University)

Frequent coauthors of Sutskever include:

  • Alec Radford
  • Aditya Ramesh
  • Mark Chen
  • Prafulla Dhariwal
  • Pranav Shyam

The scientist has published extensively in several venues, with the highest number of publications appearing in arXiv (Cornell University). Other publication venues include Leibniz-Zentrum für Informatik (Schloss Dagstuhl), Journal of Statistical Mechanics Theory and Experiment, Dagstuhl Research Online Publication Server, and Geoscientist.

Best Publications

  • ImageNet classification with deep convolutional neural networks

    Alex Krizhevsky;Ilya Sutskever;Geoffrey E. Hinton

  • Dropout: a simple way to prevent neural networks from overfitting

    Nitish Srivastava;Geoffrey Hinton;Alex Krizhevsky;Ilya Sutskever

  • Distributed Representations of Words and Phrases and their Compositionality

    Tomas Mikolov;Ilya Sutskever;Kai Chen;Greg S Corrado

  • Sequence to Sequence Learning with Neural Networks

    Ilya Sutskever;Oriol Vinyals;Quoc V. Le

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

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

  • Intriguing properties of neural networks

    Christian Szegedy;Wojciech Zaremba;Ilya Sutskever;Joan Bruna

  • TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

    Martín Abadi;Ashish Agarwal;Paul Barham;Eugene Brevdo

  • Improving neural networks by preventing co-adaptation of feature detectors

    Geoffrey E. Hinton;Nitish Srivastava;Alex Krizhevsky;Ilya Sutskever

  • Learning Transferable Visual Models From Natural Language Supervision

    Alec Radford;Jong Wook Kim;Chris Hallacy;Aditya Ramesh

  • On the importance of initialization and momentum in deep learning

    Ilya Sutskever;James Martens;George Dahl;Geoffrey Hinton

  • Language Models are Few-Shot Learners

    Tom B. Brown;Benjamin Mann;Nick Ryder;Melanie Subbiah

  • InfoGAN: interpretable representation learning by information maximizing generative adversarial nets

    Xi Chen;Yan Duan;Rein Houthooft;John Schulman

  • Recurrent Neural Network Regularization

    Wojciech Zaremba;Ilya Sutskever;Oriol Vinyals

  • Exploiting Similarities among Languages for Machine Translation

    Tomas Mikolov;Quoc V. Le;Ilya Sutskever

  • Robust Speech Recognition via Large-Scale Weak Supervision

    Unknown

  • An Empirical Exploration of Recurrent Network Architectures

    Rafal Jozefowicz;Wojciech Zaremba;Wojciech Zaremba;Ilya Sutskever

  • Generating Text with Recurrent Neural Networks

    Ilya Sutskever;James Martens;Geoffrey E. Hinton

  • Improved Variational Inference with Inverse Autoregressive Flow

    Durk P. Kingma;Tim Salimans;Rafal Jozefowicz;Xi Chen

  • Evolution Strategies as a Scalable Alternative to Reinforcement Learning.

    Tim Salimans;Jonathan Ho;Xi Chen;Ilya Sutskever

  • Evaluating Large Language Models Trained on Code

    Mark Chen;Jerry Tworek;Heewoo Jun;Qiming Yuan

  • Dota 2 with Large Scale Deep Reinforcement Learning

    Christopher Berner;Greg Brockman;Brooke Chan;Vicki Cheung

  • Zero-Shot Text-to-Image Generation

    Aditya Ramesh;Mikhail Pavlov;Gabriel Goh;Scott Gray

  • Generating Long Sequences with Sparse Transformers.

    Rewon Child;Scott Gray;Alec Radford;Ilya Sutskever

Frequent Co-Authors

Geoffrey E. Hinton
Geoffrey E. Hinton University of Toronto
Quoc V. Le
Quoc V. Le Google (United States)
Pieter Abbeel
Pieter Abbeel University of California, Berkeley
Oriol Vinyals
Oriol Vinyals DeepMind (United Kingdom)
Navdeep Jaitly
Navdeep Jaitly Google (United States)
Shixiang Gu
Shixiang Gu Google (United States)
Jeffrey Dean
Jeffrey Dean Google (United States)
Sergey Levine
Sergey Levine University of California, Berkeley

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