2026 Joshua B. Tenenbaum: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	129	102	98	63	61	633	94059

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

17406
Citations
Human-level concept learning through probabilistic program induction.

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

3264
Citations
Building machines that learn and think like people.

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

2755
Citations
How to Grow a Mind: Statistics, Structure, and Abstraction

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

2272
Citations
The large-scale structure of semantic networks: statistical analyses and a model of semantic growth.

Mark Steyvers;Joshua B. Tenenbaum

1866
Citations
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling

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

1739
Citations
Hierarchical Topic Models and the Nested Chinese Restaurant Process

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

1433
Citations
Topics in semantic representation.

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

1408
Citations
Word learning as Bayesian inference.

Fei Xu;Joshua B. Tenenbaum

1382
Citations
Causal inference in multisensory perception.

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

1238
Citations
Global Versus Local Methods in Nonlinear Dimensionality Reduction

Vin D. Silva;Joshua B. Tenenbaum

1193
Citations
Theory-based Bayesian models of inductive learning and reasoning

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

1158
Citations
Separating Style and Content with Bilinear Models

Joshua B. Tenenbaum;William T. Freeman

1084
Citations
Action understanding as inverse planning.

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

1076
Citations
Hierarchical deep reinforcement learning: integrating temporal abstraction and intrinsic motivation

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

1046
Citations
Learning systems of concepts with an infinite relational model

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

927
Citations
Probabilistic models of cognition: exploring representations and inductive biases

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

896
Citations
Simulation as an engine of physical scene understanding

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

876
Citations
Rethinking Few-Shot Image Classification: A Good Embedding is All You Need?

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

861
Citations
Optimal Predictions in Everyday Cognition

Thomas L. Griffiths;Joshua B. Tenenbaum

835
Citations
One shot learning of simple visual concepts

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

719
Citations
The Large-Scale Structure of Semantic Networks

M. Steyvers;J. Tenenbaum

13
Citations
Supplementary Material for Human-level concept learning through probabilistic program induction

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

3
Citations

Frequent Co-Authors

Jiajun Wu Stanford University

Noah D. Goodman Stanford University

Thomas L. Griffiths Princeton University

William T. Freeman MIT

Laura Schulz MIT

Antonio Torralba MIT

Samuel J. Gershman Harvard University

Peter W. Battaglia DeepMind (United Kingdom)

Michael C. Frank Stanford University

Chuang Gan University of Massachusetts Amherst

External Links

Wikipedia of Joshua B. Tenenbaum Personal website of Joshua B. Tenenbaum Google Scholar page

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