World's Best Scientists 2026 revealed!
Joshua B. Tenenbaum

Joshua B. Tenenbaum

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Computer Science
USA
2026

D-Index & Metrics

Computer Science

D-Index
129
Citations
94059
World Ranking
102
National Ranking
63

Joshua B. Tenenbaum 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 Joshua B. Tenenbaum 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: 633 publications — 97th percentile

97% of scientists in this discipline score the same or lower.

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

Joshua B. Tenenbaum 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 Joshua B. Tenenbaum 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: 129 D-Index — 99th percentile

99% 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

  • 2026 - Research.com Computer Science in United States Leader Award
  • 2025 - Research.com Computer Science in United States Leader Award
  • 2022 - Research.com Computer Science in United States Leader Award
  • 2020 - Fellow of the American Academy of Arts and Sciences

Overview

Joshua B. Tenenbaum is a researcher affiliated with MIT in the United States. Their work primarily spans the field of Computer Science, with a focus on Artificial Intelligence, Computer Vision and Pattern Recognition, and Cognitive Neuroscience. Additional areas of study include Control and Systems Engineering and Developmental and Educational Psychology.

The scientist's research covers multiple significant topics, including:

  • Multimodal Machine Learning Applications
  • Topic Modeling
  • Reinforcement Learning in Robotics
  • Human Pose and Action Recognition
  • AI-based Problem Solving and Planning
  • Explainable Artificial Intelligence (XAI)
  • Natural Language Processing Techniques

Joshua B. Tenenbaum has published extensively, with a notable presence in several key venues. These include:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Journal of Vision
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Proceedings of the National Academy of Sciences

Some recent papers authored by or associated with Joshua B. Tenenbaum cover a range of topics and publication outlets:

  • The neural architecture of language: Integrative modeling converges on predictive processing, 2021, Proceedings of the National Academy of Sciences
  • Dissociating language and thought in large language models, 2024, Trends in Cognitive Sciences
  • Neural Radiance Flow for 4D View Synthesis and Video Processing, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • A counterfactual simulation model of causal judgments for physical events, 2021, Psychological Review
  • ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation, 2020, arXiv (Cornell University)

Collaborations have been a significant part of their research output, with frequent co-authors including:

  • Chuang Gan
  • Antonio Torralba
  • Yilun Du
  • Leslie Pack Kaelbling
  • Jiayuan Mao

Joshua B. Tenenbaum's academic recognition includes being named a Fellow of the American Academy of Arts and Sciences in 2020.

Best Publications

  • A global geometric framework for nonlinear dimensionality reduction.

    J. B. Tenenbaum;V. de Silva;J. C. Langford

  • Human-level concept learning through probabilistic program induction.

    Brenden M. Lake;Ruslan Salakhutdinov;Joshua B. Tenenbaum

  • Building machines that learn and think like people.

    Brenden M. Lake;Tomer David Ullman;Joshua B Tenenbaum;Samuel J Gershman

  • How to Grow a Mind: Statistics, Structure, and Abstraction

    Joshua B. Tenenbaum;Charles Kemp;Thomas L. Griffiths;Noah D. Goodman

  • The large-scale structure of semantic networks: statistical analyses and a model of semantic growth.

    Mark Steyvers;Joshua B. Tenenbaum

  • Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling

    Jiajun Wu;Chengkai Zhang;Tianfan Xue;William T. Freeman

  • Hierarchical Topic Models and the Nested Chinese Restaurant Process

    Thomas L. Griffiths;Michael I. Jordan;Joshua B. Tenenbaum;David M. Blei

  • Topics in semantic representation.

    Thomas L. Griffiths;Mark Steyvers;Joshua B. Tenenbaum

  • Word learning as Bayesian inference.

    Fei Xu;Joshua B. Tenenbaum

  • Causal inference in multisensory perception.

    Konrad P. Körding;Ulrik Beierholm;Wei Ji Ma;Steven Quartz

  • Global Versus Local Methods in Nonlinear Dimensionality Reduction

    Vin D. Silva;Joshua B. Tenenbaum

  • Theory-based Bayesian models of inductive learning and reasoning

    Joshua B. Tenenbaum;Thomas L. Griffiths;Charles Kemp

  • Separating Style and Content with Bilinear Models

    Joshua B. Tenenbaum;William T. Freeman

  • Action understanding as inverse planning.

    Chris L. Baker;Rebecca Saxe;Joshua B. Tenenbaum

  • Hierarchical deep reinforcement learning: integrating temporal abstraction and intrinsic motivation

    Tejas D. Kulkarni;Karthik R. Narasimhan;Ardavan Saeedi;Joshua B. Tenenbaum

  • Learning systems of concepts with an infinite relational model

    Charles Kemp;Joshua B. Tenenbaum;Thomas L. Griffiths;Takeshi Yamada

  • Probabilistic models of cognition: exploring representations and inductive biases

    Thomas L. Griffiths;Nick Chater;Charles Kemp;Amy Perfors

  • Simulation as an engine of physical scene understanding

    Peter W. Battaglia;Jessica B. Hamrick;Joshua B. Tenenbaum

  • Rethinking Few-Shot Image Classification: A Good Embedding is All You Need?

    Yonglong Tian;Yue Wang;Dilip Krishnan;Joshua B. Tenenbaum

  • Optimal Predictions in Everyday Cognition

    Thomas L. Griffiths;Joshua B. Tenenbaum

  • One shot learning of simple visual concepts

    Brenden M. Lake;Ruslan Salakhutdinov;Jason Gross;Joshua B. Tenenbaum

  • The Large-Scale Structure of Semantic Networks

    M. Steyvers;J. Tenenbaum

  • Supplementary Material for Human-level concept learning through probabilistic program induction

    Brenden M. Lake;Ruslan Salakhutdinov;Joshua B. Tenenbaum

Frequent Co-Authors

Jiajun Wu
Jiajun Wu Stanford University
Noah D. Goodman
Noah D. Goodman Stanford University
Thomas L. Griffiths
Thomas L. Griffiths Princeton University
Samuel J. Gershman
Samuel J. Gershman Harvard University
Peter W. Battaglia
Peter W. Battaglia DeepMind (United Kingdom)
Michael C. Frank
Michael C. Frank Stanford University
Chuang Gan
Chuang Gan University of Massachusetts Amherst

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