World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
75
Citations
24006
World Ranking
1411
National Ranking
732

Research.com Recognitions

  • 2016 - Fellow of Alfred P. Sloan Foundation

Overview

Noah D. Goodman is a researcher affiliated with Stanford University in the United States. Their primary field of study is Computer Science, with a strong focus on Artificial Intelligence. Their work spans various aspects of AI, including subfields such as Computer Vision and Pattern Recognition, Cultural Studies, Experimental and Cognitive Psychology, and Information Systems.

The research topics covered by Noah D. Goodman include:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Speech and dialogue systems
  • Multimodal Machine Learning Applications
  • Explainable Artificial Intelligence (XAI)
  • Domain Adaptation and Few-Shot Learning
  • Language and cultural evolution

They have contributed extensively to academic literature, with a notable presence in prestigious venues. Frequent publication venues include:

  • arXiv (Cornell University)
  • Cognitive Science
  • Psychological Review
  • Open Mind
  • Proceedings of the AAAI Conference on Artificial Intelligence

Noah D. Goodman has collaboratively published with several frequent co-authors, including:

  • Robert D. Hawkins
  • Alex Tamkin
  • Gabriel Poesia
  • Mike Wu
  • Kanishk Gandhi

Their recent papers cover a range of topics and venues. Notable recent works include:

  • "On the Opportunities and Risks of Foundation Models," 2021, arXiv (Cornell University)
  • "A counterfactual simulation model of causal judgments for physical events," 2021, Psychological Review
  • "STaR: Bootstrapping Reasoning With Reasoning," 2022, arXiv (Cornell University)
  • "Polite Speech Emerges From Competing Social Goals," 2020, Open Mind
  • "From partners to populations: A hierarchical Bayesian account of coordination and convention," 2022, Psychological Review

Among their recognitions, Noah D. Goodman was named a Fellow of the Alfred P. Sloan Foundation in 2016.

Best Publications

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

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

  • On the Opportunities and Risks of Foundation Models.

    Rishi Bommasani;Drew A. Hudson;Ehsan Adeli;Russ Altman

  • Predicting pragmatic reasoning in language games.

    Michael C. Frank;Noah D. Goodman

  • The Double-Edged Sword of Pedagogy: Instruction Limits Spontaneous Exploration and Discovery

    Elizabeth Bonawitz;Patrick Shafto;Hyowon Gweon;Noah D. Goodman

  • Pyro: deep universal probabilistic programming

    Eli Bingham;Jonathan P. Chen;Martin Jankowiak;Fritz Obermeyer

  • Church: a language for generative models

    Noah D. Goodman;Vikash K. Mansinghka;Daniel Roy;Keith Bonawitz

  • One and done? Optimal decisions from very few samples.

    Edward Vul;Noah D. Goodman;Thomas L. Griffiths;Joshua B. Tenenbaum

  • Pragmatic Language Interpretation as Probabilistic Inference

    Noah D. Goodman;Michael C. Frank

  • Rational Use of Cognitive Resources: Levels of Analysis Between the Computational and the Algorithmic

    Thomas L. Griffiths;Falk Lieder;Noah D. Goodman

  • A Rational Analysis of Rule-Based Concept Learning.

    Noah D. Goodman;Joshua B. Tenenbaum;Jacob Feldman;Thomas L. Griffiths

  • Knowledge and implicature: modeling language understanding as social cognition.

    Noah D. Goodman;Andreas Stuhlmüller

  • Using Speakers' Referential Intentions to Model Early Cross-Situational Word Learning

    Michael C. Frank;Noah D. Goodman;Joshua B. Tenenbaum

  • Where science starts: Spontaneous experiments in preschoolers’ exploratory play

    Claire Cook;Noah D. Goodman;Laura E. Schulz

  • A rational account of pedagogical reasoning: Teaching by, and learning from, examples

    Patrick Shafto;Noah D. Goodman;Thomas L. Griffiths

  • Amortized Inference in Probabilistic Reasoning

    Samuel Gershman;Noah D. Goodman

  • Learning a Theory of Causality.

    Noah D. Goodman;Tomer David Ullman;Joshua B. Tenenbaum

  • The mentalistic basis of core social cognition: experiments in preverbal infants and a computational model.

    J. Kiley Hamlin;Tomer Ullman;Josh Tenenbaum;Noah Goodman

  • Learning Disentangled Representations with Semi-Supervised Deep Generative Models

    N. Siddharth;Brooks Paige;Brooks Paige;Jan-Willem van de Meent;Alban Desmaison

  • Multimodal Generative Models for Scalable Weakly-Supervised Learning

    Mike Wu;Noah D. Goodman

  • Bootstrapping in a language of thought: A formal model of numerical concept learning

    Steven T. Piantadosi;Joshua B. Tenenbaum;Noah D. Goodman

  • One and Done? Optimal Decisions from Very Few Samples

    Noah Goodman;Thomas Griffiths;Joshua Tenenbaum;Edward Vul

Frequent Co-Authors

Thomas L. Griffiths
Thomas L. Griffiths Princeton University
Michael C. Frank
Michael C. Frank Stanford University
Samuel J. Gershman
Samuel J. Gershman Harvard University
Christopher Potts
Christopher Potts Stanford University
Jamil Zaki
Jamil Zaki Stanford University
Pat Hanrahan
Pat Hanrahan Stanford University
Quentin J. M. Huys
Quentin J. M. Huys University College London
Stefano Ermon
Stefano Ermon Stanford University
David A. Lagnado
David A. Lagnado University College London

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