Curriculum Vitae of Noah D. Goodman
Stanford University
Department of Psychology
Jordan Hall, Building 01-420
450 Serra Mall
Stanford, CA 94305
Phone: 650-725-2445
Email: ngoodman@stanford.edu
Homepage: noahgoodman.net

Research Interests
Computational models of cognition.
Probabilistic programming languages.
Natural language semantics and pragmatics.
Concepts and intuitive theories.

Professional Positions
Associate Professor of Psychology and Computer Science, Stanford University, 2017 -.
(By courtesy, Associate Professor of Linguistics.)
Associate Professor of Psychology, Stanford University, 2016 - 2017.
(By courtesy, Associate Professor of Computer Science and of Linguistics.)
Assistant Professor of Psychology, Stanford University, 2010 - 2016.
(By courtesy, Assistant Professor of Computer Science and of Linguistics.)
Research Scientist, Massachusetts Institute of Technology, 2008-2010.
Post-Doctoral Associate, Massachusetts Institute of Technology, 2005-2008.
Lecturer, St. Edwards University, 2004-2005.

Education
Ph.D., Mathematics, University of Texas at Austin, 2003.
B.S. Physics, Cum Laude, University of Arizona, 1997.
B.A. Mathematics, Cum Laude, University of Arizona, 1997.

HonorsBest of IEEE Transactions on Affective Computing 2021 Paper Collection.
Best paper award, NeurIPS Workshop on Cooperative AI (2021).
Best paper award International Conference on Educational Data Mining, 2020 (EDM 2020).
2020 Cognitive Science Society paper prize for computational modeling of language.
AAAI best student paper award, 2019.
2019 Cognitive Science Society paper prize for computational modeling of language.

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Best paper award winner, ICML Workshop on Adaptive & Multitask Learning: Algorithms & Systems,
2019.
2016 Alfred P. Sloan Research Fellow in Neuroscience.
2015 Cognitive Science Society paper prize for applied computational modeling.
2014 Cognitive Science Society paper prize for computational modeling of language.
Roger N. Shepard Distinguished Visiting Scholar, 2013-14, University of Arizona.
John Philip Coghlan Fellow, 2013-14 and 2014-15.
2012 Cognitive Science Society paper prize for computational modeling of language.
2011 International Joint Conference on Artificial Intelligence best poster prize.
2011 Cognitive Science Society paper prize for computational modeling of language.
2010 J. S. McDonnell Foundation Scholar Award.
2007 Cognitive Science Society paper prize for computational modeling of higher-level cognition.
2007 Cognitive Science Society paper prize for computational modeling of perception and action.
NSF VIGRE Fellowship, 2001-2002.
University of Texas Continuing Graduate Study Fellowship, 2001-2002.
Bruton Graduate Fellowship, 2000.
National Merit Scholarship, 1994-1997.

GrantsModeling Human Geometric Reasoning with Neuro-Symbolic Systems, Stanford HAI Seed Grant, 2023.
AI Tutors to Help Prepare Students for the 21st Century Workforce, Stanford HAI Hoffman-Yee Grant,
2020-2023.
Toward Grounded, Adaptive Communication Agents, Stanford HAI Hoffman-Yee Grant, 2020.
Expeditions: Collaborative Research: Understanding the World Through Code, NSF, 2020 - 2025.
MURI: Visual Commonsense, 2019-2021 (Sub-award from UCLA).
Homo SocioNeticus: Scaling the cognitive foundations of online social behavior, 2018 - 2021 (Sub-award
from Virginia Tech).
Alfred P. Sloan Research Fellow in Neuroscience, 2016 - 2019.
Amortized Inference for Probabilistic Programs, DARPA, Oct 2013 - Jul 2017.
Grounding Lexical Meaning in Core Cognition, ONR, Sep 2013 - Mar 2017.
Development of probmods.org web-book, Stanford VPOL, 2013.
Grounded language understanding as social cognition, ONR, Jan 2013 - Jan 2016 (PI: Potts).
Embedded Humans: Provably Correct Decision Making for Networks of Humans and Unmanned Systems,
ONR, Feb 2013 - Dec 2017 (Sub-award from Berkeley, PI: Sastry; Stanford PI: Guibas).
J. S. McDonnell Foundation Scholar Award, Oct 2010 - Oct 2016.
A Framework for Core Cognition, ONR, Jul 2009 - Dec 2012 (PI: Tenenbaum).

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Publications
Peer-reviewed Journal Articles and Conference Proceedings
White, J., Goodman, N., & Hawkins, R. (2022). Mixed-effects transformers for hierarchical adaptation.
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP).
Association for Computational Linguistics.
Kreiss, E., Fang, F., Goodman, N. D., & Potts, C. (2022). Concadia: Towards image-based text generation
with a purpose. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing(EMNLP). Association for Computational Linguistics.
Geiger, A., Wu, Z., Lu, H., Rozner, J., Kreiss, E., Icard, T., Goodman, N., & Potts, C. (2022). Inducing
causal structure for interpretable neural networks. International Conference on Machine Learning,7324–7338.
Boyce, V., Hawkins, R., Goodman, N., & Frank, M. C. (2022). Two’s company but six is a crowd:
emergence of conventions in multiparty communication games. Proceedings of the Annual Meeting of the
Cognitive Science Society, 44 (44).
White, J., Burkhardt, A., Yeatman, J., & Goodman, N. (2022). Automated generation of sentence reading
fluency test items. Proceedings of the Annual Meeting of the Cognitive Science Society, 44 (44).
Poesia, G., & Goodman, N. (2022). Left to the Reader: Abstracting Solutions in Mathematical Reasoning.
Proceedings of the Annual Meeting of the Cognitive Science Society, 44 (44).
Fang, F., Sinha, K., Goodman, N. D., Potts, C., & Kreiss, E. (2022). Color Overmodification Emerges
from Data-Driven Learning and Pragmatic Reasoning. Proceedings of the Annual Meeting of the Cognitive
Science Society, 44 (44).
Tamkin, A., Nguyen, D., Deshpande, S., Mu, J., & Goodman, N. (2022). Active Learning Helps Pretrained
Models Learn the Intended Task. In A. H. Oh, A. Agarwal, D. Belgrave, & K. Cho (Eds.), Advances in
Neural Information Processing Systems.
Wu, M., & Goodman, N. (2022). Foundation Posteriors for Approximate Probabilistic Inference. Advancesin Neural Information Processing Systems.
Hsu, J., Wu, J., & Goodman, N. (2022). Geoclidean: Few-Shot Generalization in Euclidean Geometry.
Thirty-Sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track.
Mao, J., Yang, X., Zhang, X., Goodman, N., & Wu, J. (2022). CLEVRER-Humans: Describing Physicaland Causal Events the Human Way. Thirty-Sixth Conference on Neural Information Processing Systems
Datasets and Benchmarks Track.
Tamkin, A., Banerjee, G., Owda, M., Liu, V., Rammoorthy, S., & Goodman, N. (2022). DABS 2.0:
Improved Datasets and Algorithms for Universal Self-Supervision. Thirty-Sixth Conference on Neural
Information Processing Systems Datasets and Benchmarks Track.
Mu, J., Zhong, V., Raileanu, R., Jiang, M., Goodman, N., Rocktäschel, T., & Grefenstette, E. (2022).
Improving intrinsic exploration with language abstractions. Advances in Neural Information Processing
Systems.
Srivastava, M., Biyik, E., Mirchandani, S., Goodman, N., & Sadigh, D. (2022). Assistive Teaching ofMotor Control Tasks to Humans. Advances in Neural Information Processing Systems.
Zelikman, E., Wu, Y., Mu, J., & Goodman, N. (2022). STaR: Bootstrapping Reasoning With Reasoning.
Advances in Neural Information Processing Systems.

Noah D. Goodman

4Wang, R. E., Durmus, E., Goodman, N., & Hashimoto, T. (2022). Language modeling via stochastic
processes. International Conference on Learning Representations.
Hawkins, R. D., Franke, M., Frank, M. C., Goldberg, A. E., Smith, K., Griffiths, T. L., & Goodman, N.
D. (2022). From partners to populations: A hierarchical Bayesian account of coordination and convention.
Psychological Review.
Tessler, M. H., & Goodman, N. D. (2022). Warm (for Winter): Inferring comparison classes in communication. Cognitive Science.
Tessler, M. H., Tenenbaum, J., & Goodman, N. D. (2022). Logic, Probability, and Pragmatics in Syllogistic
Reasoning. Topics in Cognitive Science.
Bass, I., Bonawitz, E., Hawthorne-Madell, D., Vong, W., Goodman, N., & Gweon, H. (2022). The
effects of information utility and teachers’ knowledge on evaluations of under-informative pedagogy acrossdevelopment. Cognition, 222, 104999.
Ong, D., Soh, H., Zaki, J., & Goodman, N. (2021). Applying Probabilistic Programming to Affective
Computing. IEEE Transactions on Affective Computing. [Best of IEEE Transactions on Affective
Computing 2021 Paper Collection.]
Hawkins, R. D., Gweon, H., & Goodman, N. D. (2021). The division of labor in communication: Speakers
help listeners account for asymmetries in visual perspective. Cognitive Science, 45 (3), e12926.
Wu, M., Mosse, M., Zhuang, C., Yamins, D., & Goodman, N. (2021). Conditional Negative Sampling forContrastive Learning of Visual Representations. International Conference on Learning Representations.
Tamkin, A., Wu, M., & Goodman, N. (2021). Viewmaker Networks: Learning Views for Unsupervised
Representation Learning. International Conference on Learning Representations.
Poesia, G., & Goodman, N. (2021). Pragmatic Code Autocomplete. Proceedings of the AAAI Conferenceon Artificial Intelligence, 35.
Srivastava, M., & Goodman, N. (2021). Question Generation for Adaptive Education. Association for
Computational Linguistics (ACL).
Mu, J., & Goodman, N. (2021). Emergent Communication of Generalizations. Advances in Neural
Information Processing Systems (NeurIPS).
Poesia, G., Dong, W. X., & Goodman, N. (2021). Contrastive Reinforcement Learning of Symbolic
Reasoning Domains. Advances in Neural Information Processing Systems (NeurIPS).
Tamkin, A., Liu, V., Lu, R., Fein, D., Schultz, C., & Goodman, N. (2021). DABS: A Domain-Agnostic
Benchmark for Self-Supervised Learning. Advances in Neural Information Processing Systems (NeurIPS)
Datasets and Benchmarks Track.
Malik, A., Wu, M., Vasavada, V., Song, J., Coots, M., Mitchell, J., Goodman, N., & Piech, C. (2021).
Generative Grading: Near Human-level Accuracy for Automated Feedback on Richly Structured Problems.
Proceedings of The 14th International Conference on Educational Data Mining (EDM).
Buch, S., Fei-Fei, L., & Goodman, N. (2021). Neural Event Semantics for Grounded Language Understanding. Transactions of the Association for Computational Linguistics (TACL).
Wu, M., Goodman, N., & Ermon, S. (2021). Improving Compositionality of Neural Networks by Decoding
Representations to Inputs. Advances in Neural Information Processing Systems (NeurIPS).
White, J., Poesia, G., Hawkins, R., Sadigh, D., & Goodman, N. (2021). Open-domain clarificationquestion generation without question examples. Proceedings of the 2021 Conference on Empirical Methods
on Natural Language Processing (EMNLP).

Noah D. Goodman

5Wang, R., White, J., Mu, J., & Goodman, N. (2021). Calibrate your listeners! Robust communicationbased training for pragmatic speakers. Findings of the 2021 Conference on Empirical Methods on Natural
Language Processing (Findings of EMNLP).
Gerstenberg, T., Goodman, N., Lagnado, D., & Tenenbaum, J. (2021). A counterfactual simulation modelof causal judgments for physical events. Psychological Review.
Zhang, O., Wu, M., Bayrooti, J., & Goodman, N. (2021). Temperature as Uncertainty in Contrastive
Learning. NeurIPS Workshop on Self-Supervised Learning.
Mankewitz, J., Boyce, V., Waldon, B., Loukatou, G., Yu, D., Mu, J., Goodman, N. D., & Frank, M.
C. (2021). Multi-party referential communication in complex strategic games. NeurIPS Workshop on
Meaning in Context.
Tessler, M. H., Madeano, J., Tsividis, P. A., Harper, B., Goodman, N. D., & Tenenbaum, J. B. (2021).
Learning to solve complex tasks by growing knowledge culturally across generations. NeurIPS Workshop
on Cooperative AI. [Best paper award winner.]
Wu, Z., Geiger, A., Rozner, J., Kreiss, E., Lu, H., Icard, T., Potts, C., & Goodman, N. D. (2021). Causal
Distillation for Language Models. NAACL-HLT.
Hawkins, R. X. D., Kwon, M., Sadigh, D., & Goodman, N. D. (2020). Continual Adaptation for Efficient
Machine Communication. Proceedings of the 24th Conference on Computational Natural Language
Learning.
Mu, J., Liang, P., & Goodman, N. (2020). Shaping Visual Representations with Language for Few-shot
Classification. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.
Peloquin, B. N., Goodman, N. D., & Frank, M. C. (2020). The interactions of rational, pragmatic agentslead to efficient language structure and use. Topics in Cognitive Science, 12 (1), 433–445.
Wu, M., Choi, K., Goodman, N. D., & Ermon, S. (2020). Meta-Amortized Variational Inference andLearning. AAAI, 6404–6412.
Wu, M., Davis, R. L., Domingue, B. W., Piech, C., & Goodman, N. (2020). Variational Item Response
Theory: Fast, Accurate, and Expressive. Proceedings of The 13th International Conference on Educational
Data Mining (EDM 2020), 257–268. [Best paper award winner.]
Degen, J., Hawkins, R. D., Graf, C., Kreiss, E., & Goodman, N. D. (2020). When redundancy is useful:
A Bayesian approach to “overinformative” referring expressions. Psychological Review.
Hawkins, R. D., Goodman, N. D., Goldberg, A. E., & Griffiths, T. L. (2020). Generalizing meanings frompartners to populations: Hierarchical inference supports convention formation on networks. Proceedings ofthe 41st Annual Conference of the Cognitive Science Society. [Winner of the 2020 Cognitive Science
Society computational modeling prize for Language.]
Hawkins, R. D., Frank, M. C., & Goodman, N. D. (2020). Characterizing the dynamics of learning in
repeated reference games. Cognitive Science, 44 (6), e12845.
White, J., Mu, J., & Goodman, N. (2020). Learning to Refer Informatively by Amortizing Pragmatic
Reasoning. Proceedings of the 42nd Annual Meeting of the Cognitive Science Society.
Tamkin, A., Jurafsky, D., & Goodman, N. (2020). Language Through a Prism: A Spectral Approach forMultiscale Language Representations. Advances in Neural Information Processing Systems.
Tamkin, A., Singh, T., Giovanardi, D., & Goodman, N. (2020). Investigating Transferability in Pretrained
Language Models. Findings of the 2020 Conference on Empirical Methods on Natural Language Processing.

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Yoon, E. J., Tessler, M. H., Goodman, N. D., & Frank, M. C. (2020). Polite speech emerges fromcompeting social goals. Open Mind, 4, 71–87.
Dasgupta, I., Guo, D., Gershman, S., & Goodman, N. (2020). Analyzing Machine-Learned Representations:
A Natural Language Case Study. Cognitive Science, e12925.
Tessler, M. H., & Goodman, N. D. (2019). The Language of Generalization. Psychological Review, 126 (3),395–436.
Hawthorne-Madell, D., & Goodman, N. D. (2019). Reasoning about Social Sources to Learn from Actions
and Outcomes. Decision, 6 (1), 17—60.
Ong, D. C., Zaki, J., & Goodman, N. D. (2019). Computational models of emotion inference in Theory of
Mind: A review and roadmap. Topics in Cognitive Science.
Wu, M., Mosse, M., Goodman, N., & Piech, C. (2019). Zero Shot Learning for Code Education: Rubric
Sampling with Deep Learning Inference. Association for the Advancement of Artificial Intelligence (AAAI).
[Winner best student paper award.]
Hartshorne, J. K., de Leeuw, J. R., Goodman, N. D., Jennings, M., & O’Donnell, T. J. (2019). A thousandstudies for the price of one: Accelerating psychological science with Pushkin. Behavior Research Methods,
1–22.
Wu, M., Goodman, N., & Ermon, S. (2019). Differentiable Antithetic Sampling for Variance Reduction in
Stochastic Variational Inference. AISTATS.
Obermeyer, F., Bingham, E., Jankowiak, M., Pradhan, N., Chiu, J., Rush, A. M., & Goodman, N. D.
(2019). Tensor Variable Elimination for Plated Factor Graphs. ICML.
Degen, J., Trotzke, A., Scontras, G., Wittenberg, E., & Goodman, N. D. (2019). Definitely, maybe: A newexperimental paradigm for investigating the pragmatics of evidential devices across languages. Journal of
Pragmatics, 140, 33–48.
Scontras, G., Degen, J., & Goodman, N. D. (2019). On the grammatical source of adjective ordering
preferences. Semantics and Pragmatics, 12 (7).
Hawkins, R. X. D., Goodman, N. D., & Goldstone, R. L. (2019). The Emergence of Social Norms andConventions. Trends in Cognitive Sciences, 23 (2), 158–169.
Bingham, E., Chen, J. P., Jankowiak, M., Obermeyer, F., Pradhan, N., Karaletsos, T., Singh, R., Szerlip,
P., Horsfall, P., & Goodman, N. D. (2019). Pyro: Deep Universal Probabilistic Programming. Journal of
Machine Learning Research, 20 (28), 1–6.
Nie, A., Bennett, E., & Goodman, N. (2019). DisSent: Learning Sentence Representations from Explicit
Discourse Relations. Proceedings of the 57th Annual Meeting of the Association for Computational
Linguistics, 4497–4510. https://doi.org/10.18653/v1/P19-1442
Nie, A., Bennett, E., & Goodman, N. (2019). Learning to Explain: Answering Why-Questions
via Rephrasing. Proceedings of the First Workshop on NLP for Conversational AI, 113–120.
https://doi.org/10.18653/v1/W19-4113
Sumner, E., DeAngelis, E., Hyatt, M., Goodman, N., & Kidd, C. (2019). Cake or broccoli? Recency
biases children’s verbal responses. PloS One, 14 (6), e0217207.
Cohn-Gordon, R., & Goodman, N. D. (2019). Lost in Machine Translation: A Method to Reduce Meaning
Loss. NAACL-HLT.
McDowell, B., & Goodman, N. (2019). Learning from Omission. Proceedings of the 57th Annual Meetingof the Association for Computational Linguistics, 619–628.

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Achlioptas, P., Fan, J., Hawkins, R. X. D., Goodman, N. D., & Guibas, L. J. (2019). ShapeGlot: Learning
Language for Shape Differentiation. IEEE International Conference on Computer Vision (ICCV).
Hawkins, R. X. D., Sano, M., Goodman, N. D., & Fan, J. E. (2019). Disentangling contributions of visual
information and interaction history in the formation of graphical conventions. Proceedings of the 41st
Annual Conference of the Cognitive Science Society.
Hawkins, R. X. D., Kwon, M., Sadigh, D., & Goodman, N. D. (2019). Continual Adaptation for Efficient
Machine Communication. ICML Workshop on Adaptive & Multitask Learning: Algorithms & Systems.
[Best paper award winner.]Fan, J. E., Hawkins, R. D., Wu, M., & Goodman, N. D. (2019). Pragmatic Inference and Visual
Abstraction Enable Contextual Flexibility During Visual Communication. Computational Brain &Behavior. https://doi.org/10.1007/s42113-019-00058-7
Chopra, S., Tessler, M. H., & Goodman, N. D. (2019). The first crank of the cultural ratchet: Learningand transmitting concepts through language. Proceedings of the 41st Annual Meeting of the Cognitive
Science Society, 226–232.
Mu, J., Liang, P., & Goodman, N. (2019). Shaping Visual Representations with Language for Few-shot
Classification. NeurIPS Workshop on Visually Grounded Interaction and Language.
Peloquin, B. N., Goodman, N. D., & Frank, M. C. (2019). The interactions of rational, pragmatic
agents lead to efficient language structure and use. Proceedings of the 41st Annual Conference of the
Cognitive Science Society, 912–917. [Winner of the 2019 Cognitive Science Society computationalmodeling prize for Language.]
Foster, A., Jankowiak, M., Bingham, E., Horsfall, P., Teh, Y. W., Rainforth, T., & Goodman, N. (2019).
Variational Bayesian optimal experimental design. Advances in Neural Information Processing Systems,
14036–14047.
El Dehaibi, N., Goodman, N. D., & MacDonald, E. F. (2019). Extracting customer perceptions of productsustainability from online reviews. Journal of Mechanical Design, 141 (12). [Best paper award winner.]
Ullman, T., Stuhlmüller, A., Goodman, N., & Tenenbaum, J. (2018). Learning Physical Parameters fromDynamic Scenes. Cognitive Psychology, 104, 57–82.
Bennett, E. D., & Goodman, N. D. (2018). Extremely costly intensifiers are stronger than quite costly
ones. Cognition, 178, 147–161.
Hawkins, R. X. D., Franke, M., Smith, K., & Goodman, N. D. (2018). Emerging Abstractions: Lexicalconventions are shaped by communicative context. Proceedings of the Fortieth Annual Conference of the
Cognitive Science Society.
Ong, D. C., Goodman, N. D., & Zaki, J. (2018). Happier than thou? A self-enhancement bias in emotion
attribution. Emotion, 18 (1), 116.
Hahn, M., Degen, J., Goodman, N., Jurafsky, D., & and Richard Futrell. (2018). An information-theoretic
explanation of adjective ordering preferences. Proceedings of the Fortieth Annual Conference of the
Cognitive Science Society.
Dasgupta, I., Guo, D., Stuhlmüller, A., Gershman, S. J., & Goodman, N. D. (2018). Evaluating
Compositionality in Sentence Embeddings. Proceedings of the Fortieth Annual Conference of the Cognitive
Science Society.
Dasgupta, I., Schulz, E., Goodman, N. D., & Gershman, S. J. (2018). Remembrance of inferences past:
Amortization in human hypothesis generation. Cognition, 178, 67–81.

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Khani, F., Goodman, N. D., & Liang, P. (2018). Planning, Inference and Pragmatics in Sequential
Language Games. Transactions of the Association for Computational Linguistics (TACL).
Ouyang, L., Tessler, M. H., Ly, D., & Goodman, N. D. (2018). webppl-oed: A practical optimal experiment
design system. Proceedings of the Fortieth Annual Conference of the Cognitive Science Society.
Cohn-Gordon, R., Goodman, N. D., & and Christopher Potts. (2018). An incremental iterated responsemodel of pragmatics. Proceedings of the Society for Computation in Linguistics (SCiL).
Zhao, S., Ren, H., Yuan, A., Song, J., Goodman, N., & Ermon, S. (2018). Bias and Generalization in
Deep Generative Models: An Empirical Study. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman,
N. Cesa-Bianchi, & R. Garnett (Eds.), Advances in Neural Information Processing Systems 31 (pp.
10814–10823).
Wu, M., & Goodman, N. (2018). Multimodal Generative Models for Scalable Weakly-Supervised Learning.
In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, & R. Garnett (Eds.), Advances in
Neural Information Processing Systems 31 (pp. 5576–5586).
Cohn-Gordon, R., Goodman, N. D., & Potts, C. (2018). Pragmatically Informative Image Captioning
with Character-Level Reference. NAACL-HLT.
Scontras, G., Degen, J., & Goodman, N. D. (2017). Subjectivity predicts adjective ordering preferences.
Open Mind.
Tessler, M. H., Lopez-Brau, M., & Goodman, N. D. (2017). Warm (for winter): Comparison classunderstanding in vague language. Proceedings of the Thirty-Ninth Annual Conference of the Cognitive
Science Society.
Yoon, E. J., Tessler, M. H., Goodman, N. D., & Frank, M. C. (2017). "I won’t lie, it wasn’t amazing":
Modeling polite indirect speech. Proceedings of the Thirty-Ninth Annual Conference of the Cognitive
Science Society.
Dasgupta, I., Schulz, E., Goodman, N., & Gershman, S. (2017). Amortized Hypothesis Generation.
Proceedings of the Thirty-Ninth Annual Conference of the Cognitive Science Society.
Hawkins, R. X. D., Frank, M. C., & Goodman, N. D. (2017). Convention-formation in iterated reference
games. Proceedings of the Thirty-Ninth Annual Conference of the Cognitive Science Society.
Tessler, M. H., Goodman, N. D., & Frank, M. C. (2017). Avoiding frostbite: It helps to learn from others.
Commentary on B. Lake et al., Building machines that learn and think like people. Behavioral and Brain
Sciences, 40, e279. https://doi.org/10.1017/S0140525X17000280
Monroe, W., Hawkins, R. X. D., Goodman, N. D., & Potts, C. (2017). Colors in Context: A Pragmatic
Neural Model for Grounded Language Understanding. Transactions of the Association for Computational
Linguistics, 5 (1), 325–338.
Lieder, F., Griffiths, T. L., Huys, Q. J. M., & Goodman, N. D. (2017). Empirical evidence for resourcerational anchoring and adjustment. Psychonomic Bulletin and Review.
Lieder, F., Griffiths, T. L., Huys, Q. J. M., & Goodman, N. D. (2017). The anchoring bias reflects rationaluse of cognitive resources. Psychonomic Bulletin and Review.
Gerstenberg, T., Peterson, M. F., Goodman, N. D., Lagnado, D. A., & Tenenbaum, J. B. (2017).
Eye-tracking causality. Psychological Science, 28 (12), 1731–1744.
Scontras, G., & Goodman, N. D. (2017). Resolving uncertainty in plural predication. Cognition, 168,
294–311.

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Hawthorne-Madell, D., & Goodman, N. D. (2017). So Good It Has to Be True: Wishful Thinking in
Theory of Mind. Open Mind, 1 (2), 101–110.
Siddharth, N., Paige, B., de Meent, V., Desmaison, A., Wood, F., Goodman, N. D., Kohli, P., & Torr,
P. H. S. (2017). Learning Disentangled Representations with Semi-Supervised Deep Generative Models.
Advances in Neural Information Processing Systems 30.
Ballard, I., Miller, E., Piantadosi, S., Goodman, N., & McClure, S. (2017). Beyond Reward Prediction
Errors: Human Striatum Updates Rule Values During Learning. Cerebral Cortex.
Ritchie, D., Stuhlmüller, A., & Goodman, N. D. (2016). C3: Lightweight Incrementalized MCMC for
Probabilistic Programs using Continuations and Callsite Caching. AISTATS 2016.
Piantadosi, S. T., Tenenbaum, J. B., & Goodman, N. D. (2016). The logical primitives of thought:
Empirical foundations for compositional cognitive models. Psychological Review, 123 (4), 392–424.
Bergen, L., Levy, R., & Goodman, N. D. (2016). Pragmatic Reasoning through Semantic Inference.
Semantics and Pragmatics, 9.
Goodman, N. D., & Frank, M. C. (2016). Pragmatic language interpretation as probabilistic inference.
Trends in Cognitive Sciences, 20 (11), 818–829.
Monroe, W., Goodman, N. D., & Potts, C. (2016). Learning to Generate Compositional Color Descriptions.
Proceedings of the 2016 Conference on Empirical Methods on Natural Language Processing (EMNLP
2016).
Ritchie, D., Thomas, A., Hanrahan, P., & Goodman, N. D. (2016). Neurally-Guided Procedural Models:
Amortized Inference for Procedural Graphics Programs using Neural Networks. Advances in Neural
Information Processing Systems (NIPS 2016).
Hawkins, R. X. D., & Goodman, N. D. (2016). Conversational expectations account for apparent limits ontheory of mind use. Proceedings of the Thirty-Eighth Annual Conference of the Cognitive Science Society.
(2016)
Tessler, M. H., & Goodman, N. D. (2016). Communicating generalizations about events. Proceedings of
the Thirty-Eighth Annual Conference of the Cognitive Science Society. (2016)
Yoon, E. J., Tessler, M. H., Goodman, N. D., & Frank, M. C. (2016). Talking with tact: Polite language
as a balance between kindness and informativity. Proceedings of the Thirty-Eighth Annual Conference of
the Cognitive Science Society. (2016)
Graf, C., Degen, J., Hawkins, R. X. D., & Goodman, N. D. (2016). Animal, dog, or dalmatian? Level ofabstraction in nominal referring expressions. Proceedings of the Thirty-Eighth Annual Conference of the
Cognitive Science Society. (2016)
Ong, D. C., Zaki, J., & Goodman, N. D. (2016). Emotions in lay explanations of behavior. Proceedings of
the Thirty-Eighth Annual Conference of the Cognitive Science Society. (2016)
Franke, M., Dablander, F., Schöller, A., Bennett, E., Degen, J., Tessler, M. H., Kao, J., & Goodman,
N. D. (2016). What does the crowd believe? A hierarchical approach to estimating subjective beliefs
from empirical data. Proceedings of the Thirty-Eighth Annual Conference of the Cognitive Science Society.
(2016)
Ullman, T. D., Xu, Y., & Goodman, N. D. (2016). The Pragmatics of Spatial Language. Proceedings of
the Thirty-Eighth Annual Conference of the Cognitive Science Society. (2016)
Qing, C., Goodman, N. D., & Lassiter, D. (2016). A rational speech-act model of projective content.
Proceedings of the Thirty-Eighth Annual Conference of the Cognitive Science Society. (2016)

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Evans, O., Stuhlmüller, A., & Goodman, N. D. (2016). Learning the Preferences of Ignorant, Inconsistent
Agents. Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI-2016).
Stiller, A. J., Goodman, N. D., & Frank, M. C. (2015). Ad-hoc scalar implicature in preschool children.
Language Learning and Development, 11 (2), 176–190.
Goodman, N. D., Frank, M. C., Griffiths, T. L., Tenenbaum, J. B., Battaglia, P., & Hamrick, J. (2015).
Relevant and robust. A response to Marcus and Davis. Psychological Science, 26 (4), 539–541.
Lassiter, D., & Goodman, N. D. (2015). How many kinds of reasoning? Inference, probability, and natural
language semantics. Cognition, 136, 123–134.
Ritchie, D., Lin, S., Goodman, N. D., & Hanrahan, P. (2015). Generating Design Suggestions under Tight
Constraints with Gradient-based Probabilistic Programming. Proceedings of Eurographics 2015. [Bestpaper award honorable mention.]
Bergen, L., & Goodman, N. D. (2015). The strategic use of noise in pragmatic reasoning. Topics in
Cognitive Science, 7 (2), 336–350.
Griffiths, T. L., Lieder, F., & Goodman, N. D. (2015). Rational use of cognitive resources: Levels ofanalysis between the computational and the algorithmic. Topics in Cognitive Science, 7 (2), 217–229.
Kao, J. T., Levy, R., & Goodman, N. D. (2015). A computational model of linguistic humor in puns.
Cognitive Science. (Code at https://github.com/amoudgl/pun-model)
Lassiter, D., & Goodman, N. D. (2015). Adjectival vagueness in a Bayesian model of interpretation.
Synthese.
Bass, I., Hawthorne, D., Goodman, N. D., & Gweon, H. (2015). Not by number alone: The effect ofteacher’s knowledge and its value in evaluating "sins of omission". Proceedings of the Thirty-Seventh
Annual Conference of the Cognitive Science Society.
Bennett, E., & Goodman, N. D. (2015). Extremely costly intensifiers are stronger than quite costly ones.
Proceedings of the Thirty-Seventh Annual Conference of the Cognitive Science Society.
Degen, J., Tessler, M. H., & Goodman, N. D. (2015). Wonky worlds: Listeners revise world knowledge
when utterances are odd. Proceedings of the Thirty-Seventh Annual Conference of the Cognitive Science
Society.
Gerstenberg, T., Goodman, N. D., Lagnado, D. A., & Tenenbaum, J. B. (2015). How, whether, why:
Causal judgments as counterfactual contrasts. Proceedings of the Thirty-Seventh Annual Conference of
the Cognitive Science Society.
Hawkins, R. X. D., Stuhlmüller, A., Degen, J., & Goodman, N. D. (2015). Why do you ask? Good
questions provoke informative answers. Proceedings of the Thirty-Seventh Annual Conference of the
Cognitive Science Society.
Hawthorne, D., & Goodman, N. D. (2015). So good it has to be true: Wishful thinking in theory of mind.
Proceedings of the Thirty-Seventh Annual Conference of the Cognitive Science Society.
Icard III, T. F., & Goodman, N. D. (2015). A Resource-Rational Approach to the Causal Frame Problem.
Proceedings of the Thirty-Seventh Annual Conference of the Cognitive Science Society.
Kao, J. T., & Goodman, N. D. (2015). Let’s talk (ironically) about the weather: Modeling verbal irony.
Proceedings of the Thirty-Seventh Annual Conference of the Cognitive Science Society.
Krafft, P. M., Hawkins, R. X. D., Pentland, A., Goodman, N. D., & Tenenbaum, J. B. (2015). Emergent
Collective Sensing in Human Groups. Proceedings of the Thirty-Seventh Annual Conference of the Cognitive

Noah D. Goodman

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Science Society. [Winner of the 2015 Cognitive Science Society computational modeling prize
for Applied Cognition.]
Ong, D. C., Goodman, N. D., & Zaki, J. (2015). Near-misses sting even when they are uncontrollable.
Proceedings of the Thirty-Seventh Annual Conference of the Cognitive Science Society.
Ritchie, D., Mildenhall, B., Goodman, N. D., & Hanrahan, P. (2015). Controlling Procedural Modeling
Programs with Stochastically-Ordered Sequential Monte Carlo. SIGGRAPH 2015.
Sumner, E., DeAngelis, E., Hyatt, M., Goodman, N. D., & Kidd, C. (2015). Toddlers Always Get theLast Word: Recency biases in early verbal behavior. Proceedings of the Thirty-Seventh Annual Conferenceof the Cognitive Science Society.
Ong, D. C., Zaki, J., & Goodman, N. D. (2015). Affective Cognition: Exploring lay theories of emotion.
Cognition, 143, 141–162.
Luong, T., O’Donnell, T., & Goodman, N. D. (2015). Evaluating Models of Computation and Storage in
Human Sentence Processing. CogACLL 2015.
Stuhlmüller, A., & Goodman, N. D. (2014). Reasoning about Reasoning by Nested Conditioning: Modeling
Theory of Mind with Probabilistic Programs. J. Cognitive Systems Research, 28, 80–99.
Vul, E., Goodman, N. D., Griffiths, T. L., & Tenenbaum, J. B. (2014). One and Done? Optimal Decisions
From Very Few Samples. Cognitive Science, 38 (4), 599–637.
Frank, M. C., & Goodman, N. D. (2014). Inferring word meanings by assuming that speakers are
informative. Cognitive Psychology, 75, 80–96.
Kao, J. T., Bergen, L., & Goodman, N. D. (2014). Formalizing the pragmatics of metaphor understanding.
Proceedings of the Thirty-Sixth Annual Conference of the Cognitive Science Society.
Bergen, L., & Goodman, N. D. (2014). The strategic use of noise in pragmatic reasoning. Proceedings of
the Thirty-Sixth Annual Conference of the Cognitive Science Society. [Winner of the 2014 Cognitive
Science Society computational modeling prize for Language.]
Degen, J., & Goodman, N. D. (2014). Lost your marbles? The puzzle of dependent measures inexperimental pragmatics. Proceedings of the Thirty-Sixth Annual Conference of the Cognitive Science
Society.
Gershman, S., & Goodman, N. D. (2014). Amortized inference in probabilistic reasoning. Proceedings of
the Thirty-Sixth Annual Conference of the Cognitive Science Society.
Gerstenberg, T., Goodman, N. D., Lagnado, D. A., & Tenenbaum, J. B. (2014). From counterfactual
simulation to causal judgment. Proceedings of the Thirty-Sixth Annual Conference of the Cognitive Science
Society.
Tessler, M. H., & Goodman, N. D. (2014). Some arguments are probably valid: Syllogistic reasoning ascommunication. Proceedings of the Thirty-Sixth Annual Conference of the Cognitive Science Society.
Shafto, P., Goodman, N. D., & Griffiths, T. L. (2014). A rational account of pedagogical reasoning:
Teaching by, and learning from, examples. Cognitive Psychology, 71, 55–89.
Yang, L., Hanrahan, P., & Goodman, N. D. (2014). Generating Efficient MCMC Kernels from Probabilistic
Programs. AISTATS 2014.
Kao, J. T., Wu, J., Bergen, L., & Goodman, N. D. (2014). Nonliteral understanding of number words.
Proceedings of the National Academy of Sciences, 111 (33), 12002–12007.
Pierson, E., & Goodman, N. D. (2014). Uncertainty and denial: a resource-rational model of the value of
information. PLoS ONE, 9 (11), e113342.

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Ullman, T. D., Stuhlmüller, A., Goodman, N. D., & Tenenbaum, J. B. (2014). Learning physics fromdynamical scenes. Proceedings of the Thirty-Sixth Annual Conference of the Cognitive Science Society.
Seiver, E., Gopnik, A., & Goodman, N. D. (2013). Did she jump because she was the big sister or because
the trampoline was safe? Causal inference and the development of social attribution. Child Development,
84 (2), 443–454.
Hamlin, K. J., Ullman, T., Tenenbaum, J. B., Goodman, N. D., & Baker, C. (2013). The mentalistic basisof core social cognition: experiments in preverbal infants and a computational model. Developmental
Science, 16 (2), 209–226.
Lassiter, D., & Goodman, N. D. (2013). Context, scale structure, and statistics in the interpretation of
positive-form adjectives. Semantics and Linguistic Theory (SALT) 23, 587–610.
Goodman, N. D., & Stuhlmüller, A. (2013). Knowledge and implicature: Modeling language understanding
as social cognition. Topics in Cognitive Science, 5, 173–184.
Lieder, F., Goodman, N. D., & Huys, Q. J. M. (2013). Learned helplessness and generalization. Proceedingsof the Thirty-Fifth Annual Conference of the Cognitive Science Society.
Kao, J. T., Levy, R., & Goodman, N. D. (2013). The Funny Thing About Incongruity: A Computational
Model of Humor in Puns. Proceedings of the Thirty-Fifth Annual Conference of the Cognitive Science
Society.
Smith, N. J., Goodman, N., & Frank, M. (2013). Learning and using language via recursive pragmatic
reasoning about other agents. In C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, & K. Q.
Weinberger (Eds.), Advances in Neural Information Processing Systems (NIPS 2013) (pp. 3039–3047).
Curran Associates, Inc.
Stuhlmüller, A., Taylor, J., & Goodman, N. (2013). Learning Stochastic Inverses. Advances in Neural
Information Processing Systems (NIPS 2013).
Scontras, G., Graff, P., & Goodman, N. D. (2012). Comparing pluralities. Cognition, 123 (1), 190–197.
Piantadosi, S. T., Tenenbaum, J. B., & Goodman, N. D. (2012). Bootstrapping in a language of thought:
A formal model of numerical concept learning. Cognition, 123 (2), 199–217.
Lassiter, D., & Goodman, N. D. (2012). How many kinds of reasoning? Inference, probability, and natural
language semantics. 34th Annual Conference of the Cognitive Science Society.
Gerstenberg, T., Goodman, N., Lagnado, D. A., & Tenenbaum, J. B. (2012). Noisy Newtons: Unifying
process and dependency accounts of causal attribution. Proceedings of the Thirty-Fourth Annual Conferenceof the Cognitive Science Society.
Gerstenberg, T., & Goodman, N. D. (2012). Ping pong in Church: Productive use of concepts in humanprobabilistic inference. Proceedings of the 34th Annual Conference of the Cognitive Science Society.
Bergen, L., Goodman, N. D., & Levy, R. (2012). That’s what she (could have) said: How alternativeutterances affect language use. Proceedings of the Thirty-Fourth Annual Conference of the Cognitive
Science Society.
Goodman, N. D., & Stuhlmüller, A. (2012). Knowledge and implicature: Modeling language understanding
as social cognition. Proceedings of the Thirty-Fourth Annual Conference of the Cognitive Science Society.
[Winner of the 2012 Cognitive Science Society computational modeling prize for Language.]
Shafto, P., Goodman, N. D., & Frank, M. C. (2012). Learning from others: The consequences ofpsychological reasoning for human learning. Perspectives on Psychological Science, 7 (4), 341–351.

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Yeh, Y.-T., Yang, L., Watson, M., Goodman, N. D., & Hanrahan, P. (2012). Synthesizing open worlds
with constraints using locally annealed reversible jump MCMC. SIGGRAPH 2012, 31 (4), 56.
Frank, M. C., & Goodman, N. D. (2012). Predicting pragmatic reasoning in language games. Science,
336 (6084), 998–998.
Talton, J., Yang, L., Kumar, R., Lim, M., Goodman, N. D., & Mech, R. (2012). Learning design patterns
with bayesian grammar induction. Proceedings of the 25th Annual ACM Symposium on User Interface
Software and Technology, 63–74. [Nominated for Best Paper Award.]
Stuhlmüller, A., & Goodman, N. D. (2012). A dynamic programming algorithm for inference in recursive
probabilistic programs. Second Statistical Relational AI Workshop at UAI 2012 (StaRAI-12).
Ullman, T., Goodman, N. D., & Tenenbaum, J. B. (2012). Theory learning as stochastic search in thelanguage of thought. Cognitive Development, 27 (4), 455–480.
Lieder, F., Griffiths, T. L., & Goodman, N. D. (2012). Burn-in, bias, and the rationality of anchoring.
Advances in Neural Information Processing Systems, 2699–2707.
Goodman, N. D., Ullman, T. D., & Tenenbaum, J. B. (2011). Learning a theory of causality. Psychological
Review, 118 (1), 110.
Wingate, D., Stuhlmüller, A., & Goodman, N. D. (2011). Lightweight implementations of probabilistic
programming languages via transformational compilation. Proceedings of the 14th International Conferenceon Artificial Intelligence and Statistics, 770–778.
O’donnell, T. J., Snedeker, J., Tenenbaum, J. B., & Goodman, N. D. (2011). Productivity and reuse inlanguage. Proceedings of the Thirty-Third Annual Conference of the Cognitive Science Society. [Winnerof the 2011 Cognitive Science Society computational modeling prize for Language.]
Stiller, A., Goodman, N. D., & Frank, M. C. (2011). Ad-hoc scalar implicature in adults and children.
Proceedings of the 33rd Annual Meeting of the Cognitive Science Society.
Wingate, D., Goodman, N. D., Stuhlmueller, A., & Siskind, J. M. (2011). Nonstandard Interpretations ofProbabilistic Programs for Efficient Inference. Advances in Neural Information Processing Systems (NIPS
2011), 1152–1160.
Tenenbaum, J. B., Kemp, C., Griffiths, T. L., & Goodman, N. D. (2011). How to grow a mind: Statistics,
structure, and abstraction. Science, 331 (6022), 1279–1285.
Wingate, D., Goodman, N. D., Roy, D. M., Kaelbling, L. P., & Tenenbaum, J. B. (2011). Bayesian policy
search with policy priors. Proceedings of the Twenty-Second International Joint Conference on Artificial
Intelligence (IJCAI 11). [Winner of the Best Poster prize]
Chater, N., Goodman, N., Griffiths, T. L., Kemp, C., Oaksford, M., & Tenenbaum, J. B. (2011). The
imaginary fundamentalists: The unshocking truth about Bayesian cognitive science. Behavioral and Brain
Sciences, 34 (04), 194–196. (Commentary on Jones and Love.)
Bonawitz, E., Shafto, P., Gweon, H., Goodman, N. D., Spelke, E., & Schulz, L. (2011). The double-edged
sword of pedagogy: Instruction limits spontaneous exploration and discovery. Cognition, 120 (3), 322–330.
Cook, C., Goodman, N. D., & Schulz, L. E. (2011). Where science starts: Spontaneous experiments inpreschoolers’ exploratory play. Cognition, 120 (3), 341–349.
Ullman, T., Baker, C. L., Macindoe, O., Evans, O., Goodman, N. D., & Tenenbaum, J. B. (2010). Helpor hinder: Bayesian models of social goal inference. Advances in Neural Information Processing Systems(NIPS 2010).

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Ullman, T. D., Goodman, N. D., & Tenenbaum, J. B. (2010). Theory Acquisition as Stochastic Search.
Proceedings of Thirty Second Annual Meeting of the Cognitive Science Society.
Piantadosi, S. T., Tenenbaum, J. B., & Goodman, N. D. (2010). Beyond Boolean logic: exploring
representation languages for learning complex concepts. Proceedings of the 32nd Annual Conference of
the Cognitive Science Society, 859–864.
Stuhlmüller, A., Tenenbaum, J. B., & Goodman, N. D. (2010). Learning structured generative concepts.
Proceedings of the Thirty-Second Annual Conference of the Cognitive Science Society.
Shafto, P., Goodman, N. D., Gerstle, B., & Ladusaw, F. (2010). Prior expectations in pedagogicalsituations. Proceedings of the Thirty-Second Annual Conference of the Cognitive Science Society.
Henderson, L., Goodman, N. D., Tenenbaum, J. B., & Woodward, J. F. (2010). The Structure andDynamics of Scientific Theories: A Hierarchical Bayesian Perspective. Philosophy of Science, 77 (2),
172–200.
Frank, M., Kenney, A., Goodman, N., Tenenbaum, J., Torralba, A., & Oliva, A. (2010). Predicting object
and scene descriptions with an information-theoretic model of pragmatics. Journal of Vision, 10 (7),1241–1241.
Kemp, C., Goodman, N. D., & Tenenbaum, J. B. (2010). Learning to learn causal models. Cognitive
Science, 34 (7), 1185–1243.
Desrochers, T. M., Jin, D. Z., Goodman, N. D., & Graybiel, A. M. (2010). Optimal habits can develop
spontaneously through sensitivity to local cost. Proceedings of the National Academy of Sciences, 107 (47),20512–20517.
Frank, M. C., Goodman, N. D., Lai, P., & Tenenbaum, J. B. (2009). Informative communication in word
production and word learning. Proceedings of the 31st Annual Conference of the Cognitive Science Society.
Goodman, N. D., Baker, C. L., & Tenenbaum, J. B. (2009). Cause and intent: Social reasoning in causal
learning. Proceedings of the 31st Annual Conference of the Cognitive Science Society.
Frank, M. C., Goodman, N. D., Tenenbaum, J. B., & Fernald, A. (2009). Continuity of discourse provides
information for word learning. Proceedings of the 31st Annual Cognitive Science Society.
Schmidt, L. A., Goodman, N. D., Barner, D., & Tenenbaum, J. B. (2009). How tall is Tall? compositionality,
statistics, and gradable adjectives. Proceedings of the 31st Annual Conference of the Cognitive Science
Society, 2759–2764.
Frank, M. C., Goodman, N. D., & Tenenbaum, J. B. (2009). Using speakers’ referential intentions tomodel early cross-situational word learning. Psychological Science, 20 (5), 578–585.
Vul, E., Goodman, N. D., Griffiths, T. L., & Tenenbaum, J. B. (2009). One and done: Globally optimal
behavior from locally suboptimal decisions. Proceedings of the 31st Annual Conference of the Cognitive
Science Society.
Goodman, N. D., Ullman, T. D., & Tenenbaum, J. B. (2009). Learning a theory of causality. Proceedingsof the 31st Annual Conference of the Cognitive Science Society.
Wingate, D., Goodman, N. D., Roy, D. M., & Tenenbaum, J. B. (2009). The infinite latent events model.
Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, 607–614.
Goodman, N. D., Tenenbaum, J. B., Feldman, J., & Griffiths, T. L. (2008). A Rational Analysis ofRule-based Concept Learning. Cognitive Science, 32 (1), 108—154.
Goodman, N. D., Mansinghka, V. K., Roy, D. M., Bonawitz, K., & Tenenbaum, J. B. (2008). Church: alanguage for generative models. Uncertainty in Artificial Intelligence.

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Shafto, P., & Goodman, N. D. (2008). Teaching Games: Statistical Sampling Assumptions for Learningin Pedagogical Situations. Proceedings of the Thirtieth Annual Meeting of the Cognitive Science Society.
Piantadosi, S. T., Goodman, N. D., Ellis, B. A., & Tenenbaum, J. B. (2008). A Bayesian Model of theAcquisition of Compositional Semantics. Proceedings of Thirtieth Annual Meeting of the Cognitive Science
Society.
Kemp, C., Goodman, N. D., & Tenenbaum, J. B. (2008). Theory acquisition and the language of thought.
Proceedings of Thirtieth Annual Meeting of the Cognitive Science Society.
Baker, C. L., Goodman, N. D., & Tenenbaum, J. B. (2008). Theory-based Social Goal Inference.
Proceedings of Thirtieth Annual Meeting of the Cognitive Science Society.
Mayrhofer, R., Goodman, N. D., Waldmann, M. R., & Tenenbaum, J. B. (2008). Structured Correlationfrom the Causal Background. Proceedings of the Thirtieth Annual Conference of the Cognitive Science
Society.
Katz, Y., Goodman, N. D., Kersting, K., Kemp, C., & Tenenbaum, J. B. (2008). Modeling Semantic
Cognition as Logical Dimensionality Reduction. Proceedings of the Thirtieth Annual Conference of the
Cognitive Science Society.
Schulz, L. E., Goodman, N. D., Tenenbaum, J. B., & Jenkins, A. C. (2008). Going beyond theevidence: abstract laws and preschoolers’ responses to anomalous data. Cognition, 109 (2), 211–223.
https://doi.org/n.2008.07.017
Frank, M. C., Goodman, N. D., & Tenenbaum, J. B. (2007). A bayesian framework for crosssituational
word-learning. Advances in Neural Information Processing Systems (NIPS 2007), 20.
Goodman, N. D., Mansinghka, V., & Tenenbaum, J. B. (2007). Learning grounded causal models.
Proceedings of the Twenty-Ninth Annual Conference of the Cognitive Science Society. [Winner of the1007 Cognitive Science Society computational modeling prize for Perception and Action.]
Kemp, C., Goodman, N. D., & Tenenbaum, J. B. (2007). Learning causal schemata. Proceedings of
the Twenty-Ninth Annual Meeting of the Cognitive Science Society. [Winner of the 2007 Cognitive
Science Society computational modeling prize for Higher-level Cognition.]
Kemp, C., Goodman, N. D., & Tenenbaum, J. B. (2007). Learning and using relational theories. Advancesin Neural Information Processing Systems (NIPS 2007).
Goodman, N. D., Baker, C. L., Baraff-Bonawitz, E., Mansinghka, V. K., Gopnik, A., Wellman, H., Schulz,
L., & Tenenbaum, J. B. (2006). Intuitive theories of mind: a rational approach to false belief. Proceedingsof the Twenty-Eight Annual Conference of the Cognitive Science Society.
Giroux, E. & Goodman, N. D. (2006). On the stable equivalence of open books in three-manifolds.
Geometry & Topology.
Goodman, N. D. (2005). Overtwisted open books from sobering arcs. Algebraic and Geometric Topology.
Goodman, N. D., Griffiths, T. L, Feldman, J., and Tenenbaum, J. B. (2007). A rational analysis ofrule-based concept learning. In Proceedings of the Twenty-Ninth Annual Conference of the Cognitive
Science Society.
Henderson, L., Goodman, N. D., Tenenbaum, J. B., and Woodward, J. (2007). Frameworks in science: aBayesian approach. LSE-Pitt Conference Confirmation, Induction and Science.
Chapters
Goodman, N. D., & Lassiter, D. (2015). Probabilistic Semantics and Pragmatics: Uncertainty in Language
and Thought. In S. Lappin & C. Fox (Eds.), The Handbook of Contemporary Semantic Theory, 2nd

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Edition. Wiley-Blackwell.
Goodman, N. D., Tenenbaum, J. B., & Gerstenberg, T. (2015). Concepts in a probabilistic language ofthought. In Morgolis & Lawrence (Eds.), The Conceptual Mind: New Directions in the Study of Concepts.
MIT Press.
Goodman, N. D., Tenenbaum, J. B., Griffiths, T. L., & Feldman, J. (2008). Compositionality in rational
analysis: Grammar-based induction for concept learning. In M. Oaksford & N. Chater (Eds.), Theprobabilistic mind: Prospects for rational models of cognition. Oxford University Press.
Books (print and web)Tenenbaum, J. B., Griffiths, T. L., Chater, N., Kemp, C., Goodman, N. D., & Yuille, A. (in prep). Reverse
engineering the mind: the Bayesian approach. (in prep)
Goodman, N. D., & Stuhlmüller, A. (2015). The Design and Implementation of Probabilistic Programming
Languages. (http://dippl.org)
Goodman, N. D., & Tenenbaum, J. B. (2014). Probabilistic Models of Cognition. (https://probmods.org)
Technical Reports
Chen, J. P., Obermeyer, F., Lyapunov, V., Gueguen, L., & Goodman, N. D. (2018). Joint Mapping andCalibration via Differentiable Sensor Fusion. In arXiv preprint arXiv:1812.00880.
Hawkins, R. X. D., & Goodman, N. D. (2017). Why do you ask? The informational dynamics of questions
and answers. In Technical report: PsyArXiv:j2cp6.
Ouyang, L., Tessler, M. H., Ly, D., & Goodman, N. D. (2016). Practical optimal experiment design withprobabilistic programs. In Technical report: arXiv:1608.05046.
Ritchie, D., Horsfall, P., & Goodman, N. D. (2016). Deep Amortized Inference for Probabilistic Programs.
In Technical report: arXiv:1610.05735.
Stuhlmüller, A., Hawkins, R. X. D., Siddharth, N., & Goodman, N. D. (2015). Coarse-to-Fine Sequential
Monte Carlo for Probabilistic Programs. In Technical report: arXiv:1509.02962.
Hwang, I., Stuhlmüller, A., & Goodman, N. D. (2011). Inducing probabilistic programs by Bayesian
program merging. In Technical report: arXiv:1110.5667.
O’Donnell, T. J., Tenenbaum, J. B., & Goodman, N. D. (2009). Fragment grammars: Exploring
computation and reuse in language. In Technical Report MIT-CSAIL-TR-2009-013. Massachusetts
Institute of Technology.
McAllester, D., Milch, B., & Goodman, N. D. (2008). Random-World Semantics and Syntactic Independence for Expressive Languages (MIT-CSAIL-TR-2008-025; Number MIT-CSAIL-TR-2008-025).
Massachusetts Institute of Technology.
Other
Goodman, N. D. (2013). The principles and practice of probabilistic programming. In POPL 2013.
(Extended abstract of keynote talk.)
Goodman, N. D. (2013). Grounding Lexical Meaning in Core Cognition. (Unpublished manuscript.)
Roy, D. M., Mansinghka, V. K., Goodman, N. D., & Tenenbaum, J. B. (2008). A stochastic programming
perspective on nonparametric Bayes. In Nonparametric Bayesian Workshop, Int. Conf. on Machine
Learning (Vol. 22, p. 26).

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SoftwareWebchurch, MIT-Church, Bher, Cosh. Implementations of the Church probabilistic programming language.
WebPPL. A javascript-based probabilistic programming language.
Pyro. A deep probabilistic programming language based on Python and PyTorch.

Popular Press (selected articles)“A grand unified theory of AI,” MIT News, March 30, 2010.
“I, algorithm.” New Scientist, January 29, 2011.
“More Than Child’s Play: Ability to Think Scientifically Declines as Kids Grow Up.” Scientific American,
September 21, 2011.
“Artificial Intelligence Could Be on Brink of Passing Turing Test.” WIRED, April 12, 2012.
“Context is key to making computers better conversationalists.” WIRED.uk, June 20, 2012.
“Forget the Turing Test: Here’s How We Could Actually Measure AI.” WIRED, June 12, 2014.
“Solve For Standing Ovation: Should AI Researchers Bother Building A TED-Bot?” Popular Science,
March 28, 2014.
“This Computer Knows When ‘Literally’ Isn’t Literal.” Discover, August 5, 2014.
“Why Can’t Robots Understand Sarcasm?” The Atlantic, January 22, 2015.
“Think you’re punny? Computer that can tell how good a joke is.” New Scientist, August 12, 2015.
“Call it Clement Droid: a machine that has a droll sense of humour.” The Times, August 15, 2015.
“What people can learn from algorithms — and algorithms can learn from people.” Boston Globe, May 27,
2016.
“AI’s Langauge Problem.” MIT Technology Review, August 9, 2016.

Invited Presentations
SIGNLL Conference on Computational Natural Language Learning, keynote, December 2022.
NeurIPS workhop “Math AI”, December 2022
NeurIPS workshop “Langauge in Reinforcement Learning”, December 2022.
Duolingo Colloquium, October 2022.
Royal Society Hooke Symposium “Cognitive AI”, September 2022.
International Joint Conferences on Learning and Reasoning, keynote, September 2022.
University College London workshop “Human Behavioral AI”, September 2022.
University of Chicago Decision Seminar, May 2022.
University of Washington Natural Language Processing seminar, December 2021.
Tsinghua Introduction to AI guest lecture, November 2021.
UIUC Quantitative Psychology Seminar, November 2021.
ICCV workshop 3D Representations of Scenes, October 2021.

Noah D. Goodman

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WPI CS colloquium, October 2021.
CVPR workshop Language for 3D Scene Understanding, June 2021.
AAAI Symposium “Conceptual Abstraction and Analogy in Natural and Artificial Intelligence”, November
2020.
MPI Tübingen, November 2020.
Brown CLIPS “Social Cognitive Seminar”, October 2020.
“Optimal experiment design” workshop, Mathematical Psychology, July 2020.
University of Saarbrucken, Germany, February 2020.
ICLR, New Orleans, LA, May 2019.
Open Source Leadership Summit, Half Moon Bay, CA, March 2019.
College de France, Paris, France, February 2019.
“Robots Seminar”, Oxford, UK, February 2019.
Deepmind, London, UK, February 2019.
Probabilistic Programming, Cambridge, MA, October 2018.
KogWis (Germany Cognitive Science Society Biannual Conference), Darmstadt, Germany, September
2018.
“Program Induction” workshop, CogSci, Madison, WI, July 2018.
A-Star, Singapore, June 2018.
Dubrovnik Cognitive Science Conference, Dubrovnik, Croatia, May 2018.
Cognitive Science Colloquium, Johns Hopkins University, Baltimore, MD, March 2018.
“Emergent Language” workshop, NIPS, Long Beach, CA, November 2017.
“Combining Academia and Industry Workshop”, CogSci, London, UK, July 2017.
Gatsby Unit, London, UK, July 2017.
PLEMM workshop, Facebook, Menlo Park, CA, May 2017.
EmTech Digital, San Francisco, CA, March 2017.
Uber Technology Conference, Uber, Palo Alto, CA, February 2017.
Machine Learning Seminar, University of Washington, Seattle, WA, January 2017.
Tokyo, Japan, December 2016.
Society for Philosophy and Psychology, Austin, TX, May 2016.
Adobe Machine Learning Seminar, San Jose, CA, April 2016.
DGfS workshop on Computational Pragmatics, Konstanz, Germany, February 2016.
SPLAP Workshop, UCSC, Santa Cruz, CA, February 2016.
Linguistic Universals Colloquium, Harvard University, Cambridge, MA, October 2015.
CS, Brown University, Providence, RI, October 2015.
CLPS Colloquium, Brown University, Providence, RI, October 2015.

Noah D. Goodman

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ILLC Colloquium, University of Edinburgh, Edinburgh, UK, September 2015.
XPRAG 7, Plenary Speaker, Chicago, IL, July 2015.
Microsoft Faculty Summit, Seattle, WA, July 2015.
AAAI symposium on Knowledge Representation and Reasoning, Stanford, CA, March 2015.
UCSD seminar on Computational and Experimental Pragmatics, San Diego, CA, February 2015.
Princeton Cognitive Science Colloquium, Princeton, NJ, January 2015.
University of Maryland Cognitive Science Colloquium, College Park, MD, January 2015.
University of Maryland NLP Seminar, College Park, MD, January 2015.
Northwestern University Linguistics Colloquium, Evanston, IL, January 2015.
Cognitive Science Society invited symposium “Foundations of Social Cognition”, Quebec City, Canada,
July 2014.
NYU Psychology, New York, April 2014.
DE Shaw Tech Talk, New York, April 2014.
University of Arizona Cognitive Science Colloquium, Tucson, AZ, February 2014.
AI Colloquium, Groningen, Netherlands, February 2014.
Amsterdam Colloquium, Amsterdam, Netherlands, December 2013.
NeuroSpin, Paris, France, December 2013.
IIIS Machine Learning Seminar, Tsinghua University, Beijing, China, October 2013.
“Logic across the university” workshop, Tsinghua University, Beijing, China, October 2013.
Intelligence Initiative Seminar, MIT, Cambridge, MA, September 2013.
Laboratory for Developmental Science Seminar, Harvard, Cambridge, MA, September 2013.
CogSci workshop “Producing Referring Expressions”, Berlin, Germany, August 2013.
CogSci workshop “Motivations and Goals in Developing Integrative Models of Human Cognition”, Berlin,
Germany, August 2013.
“Rational Choice Workshop”, Dept. of Economics, University of Chicago, Chicago, IL, May 2013.
UT-Austin Linguistics Colloquium, Austin, TX, April 2013.
UT-Austin Cognitive Systems Forum, Austin, TX, April, 2013.
Google, Mountain View, CA, April 2013.
Intel, Sunnyvale, CA, April 2013.
IMBS workshop “Quantum thinking”, Irvine, CA, February 2013.
Keynote, Principles of Programming Languages (POPL 13), Rome, Italy, January 2013.
Linguistics Colloquium, Tubingen, Germany, January 2013.
Invited Symposium, Budapest CEU Conference on Cognitive Development, Budapest, Hungary, January
2013.
Indiana Cognitive Science Colloquium, Bloomington, IN, November 2012.

Noah D. Goodman

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Statistical Relational Artificial Intelligence workshop, UAI, Avalon, CA, August 2012.
Early Career Keynote Speaker, International Conference on Thinking, symposium on Causal Learning &Reasoning, London 2012.
Early Career Keynote Speaker, International Conference on Thinking, symposium on Inductive Reasoning,
London 2012.
Reasoning and Interaction workshop, UT-Austin, Austin, TX, June 2012.
Workshop “Interdisciplinary approaches to implicature.” MIT, Cambridge, MA, May 2012.
California Cognitive Science Conference, Berkeley, CA, April 2012.
International Congress on Computer Vision, Vision Grammars Workshop, Barcelona, Spain, November
2011.
UC Berkeley Institute for Human Development seminar, Berkeley, CA, October 2011.
UC Berkeley Institute for Cognitive and Brain Sciences seminar, Berkeley, CA, September 2011.
Gatsby Unit special seminar, University College, London, UK, September 2011.
London Judgment and Decision Making seminar, London, UK, September 2011.
AAAI workshop on Plan and Intent Recognition, San Francisco, CA, August 2011.
UC Merced Cognitive and Information Sciences Colloquium, Merced, CA, March 2011.
UCSC Psychology Colloquium, Santa Cruz, CA, February 2011.
Neural Information Processing Systems workshop “Modeling human communication dynamics”. Whistler,
BC, December 2010.
SRI, Menlo Park, CA, November 2010.
UCSD Psychology Colloquium, San Diego, CA, November 2010.
CSLI Symposium, Stanford University, Stanford, CA, October 2010.
Humanity+ Summit, Harvard University, Cambridge, MA, June 2010.
Cornell Workshop on Grammar Induction, Ithaca, NY, May 2010.
Massachusetts General Hospital, Biostatistics Seminar. Boston, MA, March 2010.
Johns Hopkins University, Psychology Department special seminar. Baltimore, MD, January 2010.
Stanford University, Psychology Department special seminar. Stanford, CA, January 2010.
University of Rochester, Brain and Cognitive Sciences colloquium. Rochester, NY, October 2009.
University of Michigan, Developmental Psychology Brown Bag seminar. Ann Arbor, MI, October 2009.
Brown University, Pattern Theory seminar. Providence, RI, October 2009.
University of Edinburgh, Informatics Division colloquium. Edinburgh, UK, July 2009.
Banff International Research Station workshop “Probabilistic models of cognitive development”. Banff,
BC, May 2009.
Invited commentary, Interdisciplinary Graduate Conference on Consciousness. Boston, MA, April 2009.
MIT, Brain and Cognitive Sciences special seminar. Boston, MA, March 2009.

Noah D. Goodman

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Neural Information Processing Systems workshop “Probabilistic programming”, Whistler, BC, December
2008.
Neural Information Processing Systems workshop “Human learning meets machine learning”. Whistler,
BC, December 2008.
New York University, Developmental Psychology seminar. New York, NY, October 2008.
Keynote speaker, International Conference on Inductive Logic Programming. Prague, September 2008.
University of Texas, Cognitive Psychology seminar. Austin, TX, August 2008.
Center for Advanced Study in the Behavioral Sciences workshop “Early mechanisms of understandingsocial causation” (Festschrift for John S. Watson). Stanford, CA, April 2008.
International Conference on Infant Studies invited symposium “From statistical regularities to conceptual
inference”. Vancouver, BC, March 2008.
ONR Workshop on Computational Social Cognition. MIT, Cambridge, MA, March 2008.
Harvard university, Psychology colloquium. Cambridge, MA, February 2008.
University of California, Berkeley, Computational Cognitive Science seminar. Berkeley, CA, November
2007.
AAAI Fall Symposia workshop “Representation Change”. Washington, DC, November 2007.
Society for Philosophy and Psychology. Toronto, ON, June 2007. (Invited commentary on D. Lyons,“Covert Rationality: Overimitation and the Structure of Children’s Causal Learning”.)
McDonnell Foundation Workshop on Moral Cognition. Pasadena, CA, May 2007.
University of Salzburg, Institute fur Psychologie colloquium. Salzburg, AU, April 2007.
University of Gottingen, Cognitive and Decision Sciences seminar. Gottingen, GM, April 2007.
Rutgers University, Center for Cognitive Science seminar. Piscataway, NJ, March 2007.
Society for Philosophy and Psychology, Invited symposium on Causality. St. Louis, MO, June 2006.
University of California, Berkeley, Cognitive Development seminar. Berkeley, CA, 2006.
University of Michigan, Developmental Psychology seminar. Ann Arbor, MI, 2006.
Brown University, Cognitive Science seminar. Providence, RI, 2005.
M.I.T., Computational Cognitive Science seminar. Cambridge, MA, December 2004.
Bryn Mawr College, Contact Topology seminar. Bryn Mawr, PA, April 2003.
University of Pennsylvania, Department of Mathematics Geometry-Topology seminar. Philadelphia, PA,
January 2003.
University of Texas at Austin, Department of Mathematics Topology seminar. Austin, TX, March 2002.
Columbia University, Department of Mathematics Topology seminar. New York, NY, March 2001.
State University of New York, Department of Mathematics Geometry seminar. Stony Brook, NY, March
2001.

Noah D. Goodman

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Professional Services
Journal Reviewer (selected): Science. Nature. PNAS. Cognition. Trends in Cognitive Science. Cognitive
Science. Cognitive Psychology. Child Development. Memory and Cognition. Journal of Mathematical
Psychology. Cognitive Processing. Journal of Experimental Psychology: Learning, Memory, & Cognition.
Journal of Experimental Psychology: General. Philosophical Transactions A. American Journal of
Psychology. Cerebral Cortex. Decision. Natural Language Semantics.
Conference Proceedings Reviewer (selected): Cognitive Science. Neural Information Processing Systems.
International Conference on Learning Representations. Society for Philosophy and Psychology. Uncertainty
in Artificial Intelligence.
Grant reviewer: NSF (ad-hoc and panelist), ONR.
Program committee / Senior PC: Cognitive Science Society, 2014, 2015, 2016. AAAI 2016. AIStats 2017,etc.
Co-organizer: Cognitive Science Society, symposium “Empirical and Computational Approaches toMetaphor and Figurative Meaning”, Pennsylvania, August 2016.
Co-organizer: Cognitive Science Society, symposium “The Emergence of Conventions”, Pennsylvania,
August 2016.
Co-Organizer: European Summer School of Logic Language and Information, workshop “Formal andExperimental Pragmatics”. Tubingen, August 2014.
Organizer: Stanford Pragmatics and Social Cognition Workshop, March 2013.
Co-Organizer: NIPS workshop on Probabilistic Programming, December 2012.
Area chair: NIPS 2011. Granada, Spain, December 2011.
Co-Organizer: IPAM summer school “Probabilistic Models of Cognition”. Los Angeles, CA, July 2011.
Organizer: workshop “Probabilistic Programming in AI”. Los Altos, CA, January 2010.
Co-Organizer: NIPS workshop “Bounded-rational analyses of human cognition: Bayesian models, approximate inference, and the brain”. Whistler, BC, December 2009.
Program committee: International Workshop on Statistical Relational Learning. Leuven, Belgium, July2009.
Co-Organizer: Cognitive Science Society, workshop “Intuitive pedagogical reasoning: an interdisciplinary
workshop”. Amsterdam, Netherlands, August 2009.
Co-Organizer: Annual Summer Interdisciplinary Conference, symposium “Bayesian models in psychology”.
Valle ‘Aosta, Italy, July 2009.
Co-Organizer: Eastern Psychology Association, workshop “Social routes to causal knowledge: action,
imitation, and pedagogy”. Boston, MA, March 2008.
Co-Organizer: McDonnell Foundation workshop “Explanation and prior knowledge”. Cambridge, MA,
November 2006.
Mentor (1999-2001), Canada/USA Mathcamp.
Co-Organizer and Coordinator of Hiring, Canada/USA Mathcamp 2000, 2001.

Noah D. Goodman

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Outreach
Guest lecture, Summer School in Cognitive Science, Montreal, Canada, June 2016.
Guest lecturer, SAILORS program, Stanford, 2015, 2016, 2017, 2021.
Studium Generale, Groningen, Netherlands, February 2014.
Visiting Lecturer (2006, 2008, 2010, 2011, 2014), Canada/USA Mathcamp.

Teaching
University Courses
Psychology of the Climate Crisis (PSYCH 278). Spring 2023.
Probabilistic models of cognition: Reasoning and Learning (PSYCH 220A, CS 428A). Spring 2023.
Probabilistic Models of Cognition: Language (PSYCH 220B, CS 428B, LINGUIST 238B). Autumn 2021.
“Mind and Machines” (SymSys 1). Winter 2021, 2022, 2023.
Levels of Analysis in Cognitive Science (PSYCH 296, PHIL 366). Autumn 2021.
“Langauge and Thought” (OSPMADRD 19). Stanford (BOSP Madrid). Winter 2020.
“Independent Study in Machine Translation” (OSPMADRD 20). Stanford (BOSP Madrid). Winter 2020.
“Psychometrics and automated experiment design” (PSYCH 241). Stanford. Autumn 2019.
“Langauge and Thought” (Psych 132). Stanford. Spring 2019.
“Computation and Cognition: the Probabilistic Approach” (Psych 204 / CS 428). Stanford. Winter 2011,
Winter 2012, Autumn 2012, Autumn 2013, Spring 2015, Autumn 2016, Autumn 2017, Autumn 2018,
Spring 2021.
“Topics in Natural and Artificial Intelligence”. Stanford. Autumn 2018.
“Seminar on the Science of Meditation” (Psych 295). Stanford. Spring 2018.
“Seminar in Semantics: Formal semantics and the psychology of reasoning” (LINGUIST 236/PSYCH
236). Stanford. Spring 2017.
“Foundations of Cognition” (Psych 205). Stanford. Spring 2015.
“Probabilistic Models of Social Behavior and Affect” (Psych 241). Stanford. Spring 2014. (Co-taught
with J. Zaki, M. Frank.)“Representations of Meaning” (Psych 236 c, Linguist 236). Stanford. Spring 2013.(Co-taught with C.
Potts.)“Introduction to Cognitive Science” (SymSys 100, Psych 34). Stanford. Spring 2012, Winter 2013, Winter2014.
“Formal and Computational Approaches in Psychology and Cognitive Science” (Psych 239). Stanford.
Spring 2011. (Co-taught with J. McClelland.)Co-taught (with L. Schulz and C. Moore): “Perception, Conception, and Action: Grounding Thoughts inExperience (and Vice Versa)”, MIT, Spring 2008.
Experience teaching mathematics at all levels, 1997-2005. (Details by request.)

Noah D. Goodman

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Summer Schools and Tutorials
Probabilistic Programming for Advanced Machine Learning (summer school for DARPA program of thesame name), Portland, August 2016.
“Probabilistic programming languages.” European Summer School of Logic Language and Information.
Tubingen, August 2014.
“Probability in semantics and pragmatics” (with D. Lassiter). European Summer School of Logic Language
and Information. Dusseldorf, August 2013.
“Stochastic lambda calculus and its applications in cognitive science.” (Invited course.) North-American
Summer School of Logic, Language, and Information. Austin, TX, June 2012.
“Computational Cognitive Science: Probability, Programs, and the Mind.” European Summer School of
Logic Language and Information. Copenhagen, August 2010.
Tutorial on probabilistic models of cognition (with T. O’Donnell). Cornell University, October 2009.
IPAM Graduate Summer School: “Probabilistic Models of Cognition: The Mathematics of Mind”, Los
Angeles CA, July 2007. ( 3 lectures.)

Advising
Post-doctoral students
Judy Fan, 2017-2019 (Now assistant professor of Psychology, UCSD)
Daniel Ritchie, 2016-17 (Now assistant professor of CS, Brown)
Leon Bergen, 2016-2017 (Now assistant professor of Linguistics, UCSD)
Judith Degen, 2013-2017 (Now assistant professor of Linguistics, Stanford)
Andreas Stuhmueller, 2015-2017 (Now CEO Ought, Inc.)
Long Ouyang, 2015-2016
Gregory Scontras, 2014-2016 (Now associate professor of Linguistics, UC Irvine)
Siddarth Narayanaswami 2013-15 (Now lecturer, University of Edinburgh)
Daniel Ly, 2013-15
Daniel Lassiter, 2011-13 (Now assistant professor of Linguistics, Stanford University)
Joseph Austerweil, 2013 (Now associate professor of Cognitive Science, University of Wisconsin)
Ph.D. students
Kanishk Gandhi (Stanford, Computer Science)
Eric Zelikman (Stanford, Computer Science)
Ben Prystawski (Stanford, Psychology)
Rose Wang (Stanford, Computer Science)
Gabriel Poesia (Stanford, Computer Science)
Julia White (Stanford, Electrical Engineering)Alex Tamkin (Stanford, Computer Science)Noah D. Goodman

Jesse Mu (Stanford, Computer Science)
Mike Wu (Stanford, Computer Science), completed 2022
Erin Bennett (Stanford, Psychology), completed 2020
Robert X. D. Hawkins (Stanford, Psychology), completed 2019
Michael Henry Tessler (Stanford, Psychology), completed 2018
Desmond Ong (Stanford, Psychology), completed 2017
Daniel Ritchie (Stanford, Computer Science), completed 2016
Justine Kao (Stanford, Psychology), completed 2016
Long Ouyang (Stanford, Psychology), completed 2015
Daniel Hawthorne (Stanford, Psychology), completed 2015
Andreas Stuhmueller (MIT, BCS), completed 2015
Ph.D. Committees
Eric Mitchell (Stanford, CS)
Andrew Nam (Stanford, Psychology)
Shyamal Buch (Stanford, CS), completed 2022
Shao-Fang (Pam) Wang (Stanford, Psychology), completed 2021
Natalia Velez (Stanford, Psychology), completed 2019
Andrew Lampinen (Stanford, Psychology), completed 2019
Erica Yoon (Stanford, Psychology), completed 2019
Ziang Xie (Stanford, CS), completed 2018
Justin Johnson (Stanford, CS), completed 2018
Will Munroe (Stanford, CS), completed 2018
Steven Hansen (Stanford, Psychology), completed 2018
Sida Wang (Stanford, CS), completed 2017
Ian Ballard (Stanford, Neuroscience), completed 2017
Molly Lewis (Stanford, Psychology), completed 2016
Leon Bergen (MIT, BCS), completed 2015
Lingfeng Yang (Stanford, Computer Science), completed 2015
Rahul Sharma (Stanford, Computer Science), completed 2015
Eric Schkufza (Stanford, Computer Science), completed 2015
Eric Miller (Stanford, Psychology), completed 2015
Tomer Ulman (MIT, BCS), completed 2014
Spence Green (Stanford, Computer Science), completed 2014
Ranjitha Kumar (Stanford, Computer Science), completed 201325

Noah D. Goodman

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Yi-Ting Yeh (Stanford, Computer Science), completed 2013
Thomas Icard III (Stanford, Philosophy), completed 2013
Jerry Talton III (Stanford, Computer Science), completed 2012
Jeremy Glick (Stanford, Psychology), completed 2011
Daniel Sternberg (Stanford, Psychology), completed 2011
Steve Piantadosi (MIT, BCS), completed 2011
Timothy J. O’Donnell (Harvard, Psychology), completed 2011

University Committees
Psychology Department, Graduate Admissions Committee (Chair), 2018-19, 2019-20, 2020-2021, 20212022, 2022-2023.
Committee on Review of Undergraduate Majors, 2021-22, 2022-23.
Psychology Department, Cognitive and Developmental Search Committee (Chair), 2021-22.
CS Department, Pre-Hiring Committee, 2020-21.
Human-centered AI Initiative Design Committee, 2018-19.
CS Department, Curriculum Revision Committee, 2018-19.
CS Department, Graduate Admissions Committee, 2018-19.
CS Department, Ad-Hoc Promotion Committee, 2018-19.
Psychology Department, Graduate Admissions Committee, 2016-17, 2017-18.
CS Department, MA Advising Committee, 2017-18.
Psychology Department, Colloquium Committee, 2014-2015.
Psychology Department, Cognitive Search Committee, 2014-2015.
Psychology Department, Curriculum Committee, 2014-2015.
Psychology Department, Colloquium Committee, 2013-2014.
Human Subjects Research IRB, 2013-2014.
Psychology Department, Colloquium Committee, 2012-2013.
Psychology Department, Graduate Admissions Committee, 2012-2013.
Psychology Department, Cognitive Search Committee, 2012-2013.
Psychology Department, Computer Committee, 2012-2013.
Psychology Department, Graduate Program Committee, 2011-2012.
Psychology Department, Graduate Admissions Committee, 2011-2012.
Psychology Department, Cognitive Search Committee, 2011-2012.
Psychology Department, Computer Committee, 2011-2012.
Psychology Department, Cognitive Search Committee, 2010-2011.

Noah D. Goodman

Miscellaneous:
Citizen of the USA.
Member Cognitive Science Society.
Member Psychonomic Society.

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