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Curriculum Vitae of Noah D. Goodman

Department of Psychology
450 Jane Stanford Way
Building 420
Stanford University
Stanford, CA 94305

ngoodman@stanford.edu
noahgoodman.net

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.

Honors

• Best 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.

• 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.

Grants

• Modeling 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).

Publications

Peer-reviewed Journal Articles and Conference Proceedings

• Gandhi, K., Li, M. Y., Goodyear, L., Li, L., Bhaskar, A., Zaman, M., & Goodman, N. D. (2025-Preprint). BoxingGym: Benchmarking Progress in Automated Experimental Design and Model Discovery. ArXiv Preprint ArXiv:2501.01540.

• He-Yueya, J., Ma, W. A., Gandhi, K., Domingue, B. W., Brunskill, E., & Goodman, N. D. (2024-Preprint). Psychometric alignment: Capturing human knowledge distributions via language models. ArXiv Preprint ArXiv:2407.15645.

• Hsu, J., Mao, J., Tenenbaum, J. B., Goodman, N. D., & Wu, J. (2024-Preprint). What Makes a Maze Look Like a Maze? ArXiv Preprint ArXiv:2409.08202.

• Gandhi, K., Lynch, Z., Fränken, J.-P., Patterson, K., Wambu, S., Gerstenberg, T., Ong, D. C., & Goodman, N. D. (2024-Preprint). Human-like Affective Cognition in Foundation Models. ArXiv Preprint ArXiv:2409.11733.

• Arora, A., Jurafsky, D., Potts, C., & Goodman, N. D. (2024-Preprint). Bayesian scaling laws for in-context learning. ArXiv Preprint ArXiv:2410.16531.

• Li, M. Y., Vajipey, V., Goodman, N. D., & Fox, E. B. (2024-Preprint). CriticAL: Critic Automation with Language Models. ArXiv Preprint ArXiv:2411.06590.

• Poesia, G., Gandhi, K., Zelikman, E., & Goodman, N. D. (2024). Certified Deductive Reasoning with Language Models. Transactions on Machine Learning Research.

• Wang, R., Zelikman, E., Poesia, G., Pu, Y., Haber, N., & Goodman, N. D. (2024). Hypothesis Search: Inductive Reasoning with Language Models. The Twelfth International Conference on Learning Representations (ICLR).

• Arumugam, D., Ho, M. K., Goodman, N. D., & Van Roy, B. (2024). Bayesian reinforcement learning with limited cognitive load. Open Mind, 8, 395–438.

• Geiger, A., Wu, Z., Potts, C., Icard, T., & Goodman, N. D. (2024). Finding Alignments Between Interpretable Causal Variables and Distributed Neural Representations. Causal Learning and Reasoning (CLEAR).

• Wang, R. E., Wirawarn, P., Khattab, O., Goodman, N., & Demszky, D. (2024). Backtracing: Retrieving the Cause of the Query. EACL 2024, Long Paper Findings.

• Wu, Z., Geiger, A., Arora, A., Huang, J., Wang, Z., Goodman, N. D., Manning, C. D., & Potts, C. (2024). pyvene: A library for understanding and improving PyTorch models via interventions. NAACL Demo.

• Fränken, J.-P., Gandhi, K., Qiu, T., Khawaja, A., Goodman, N. D., & Gerstenberg, T. (2024). Procedural Dilemma Generation for Evaluating Moral Reasoning in Humans and Language Models. Proceedings of the 46th Annual Conference of the Cognitive Science Society (Oral).

• Li, M. Y., Fox, E. B., & Goodman, N. D. (2024). Automated Statistical Model Discovery with Language Models. International Conference on Machine Learning (ICML).

• Tamkin, A., Taufeeque, M., & Goodman, N. D. (2024). Codebook features: Sparse and discrete interpretability for neural networks. International Conference on Machine Learning (ICML).

• He-Yueya, J., Goodman, N. D., & Brunskill, E. (2024). Evaluating and Optimizing Educational Content with Large Language Model Judgments. International Educational Data Mining Society.

• Zelikman, E., Harik, G., Shao, Y., Jayasiri, V., Haber, N., & Goodman, N. D. (2024). Quiet-star: Language models can teach themselves to think before speaking. Conference on Language Modeling (COLM).

• Andukuri, C., Fränken, J.-P., Gerstenberg, T., & Goodman, N. D. (2024). Star-gate: Teaching language models to ask clarifying questions. Conference on Language Modeling (COLM).

• Gandhi, K., Lee, D., Grand, G., Liu, M., Cheng, W., Sharma, A., & Goodman, N. D. (2024). Stream of Search (SoS): Learning to Search in Language. Conference on Language Modeling (COLM), Oral.

• Fränken, J.-P., Zelikman, E., Rafailov, R., Gandhi, K., Gerstenberg, T., & Goodman, N. D. (2024). Self-Supervised Alignment with Mutual Information: Learning to Follow Principles without Preference Labels. Advnces in Neural Information Processing Systems (NeurIPS).

• Poesia, G., Broman, D., Haber, N., & Goodman, N. D. (2024). Learning Formal Mathematics From Intrinsic Motivation. Advances in Neural Information Processing Systems (NeurIPS), Oral.

• Mishra, S., Poesia, G., Mo, B., & Goodman, N. D. (2024). MathCAMPS: Fine-grained Synthesis of Mathematical Problems From Human Curricula. NeurIPS Workshop on MathAI.

• Boyce, V., Hawkins, R. D., Goodman, N. D., & Frank, M. C. (2024). Interaction structure constrains the emergence of conventions in group communication. Proceedings of the National Academy of Sciences, 121(28), e2403888121.

• Kenton, Z., Siegel, N. Y., Kramár, J., Brown-Cohen, J., Albanie, S., Bulian, J., Agarwal, R., Lindner, D., Tang, Y., Goodman, N. D., & others. (2024). On scalable oversight with weak llms judging strong llms. Advances in Neural Information Processing Systems (NeurIPS).

• Feng, S. Y., Goodman, N. D., & Frank, M. C. (2024). Is Child-Directed Speech Effective Training Data for Language Models? Proceedings of the 2024 Conference on Empirical Methods on Natural Language Processing (EMNLP).

• Ong, D. C., Zhi-Xuan, T., Tenenbaum, J. B., & Goodman, N. D. (2024). Probabilistic programming versus meta-learning as models of cognition. The Behavioral and Brain Sciences, 47, e158.

• Tamkin, A., Handa, K., Shrestha, A., & Goodman, N. (2023). Task Ambiguity in Humans and Language Models. The Eleventh International Conference on Learning Representations (ICLR).

• Poesia, G., & Goodman, N. D. (2023). Peano: Learning Formal Mathematical Reasoning. Phil. Trans. of the Royal Society A.

• Hawkins, R. D., Berdahl, A., Pentland, A. S., Goodman, N. D., Tenenbaum, J. B., & Krafft, P. M. (2023). Flexible social inference facilitates targeted social learning when rewards are not observable. Nature Human Behavior.

• Hawkins, R. D., Sano, M., Goodman, N. D., & Fan, J. E. (2023). Visual resemblance and interaction history jointly constrain pictorial meaning. Nature Communications, 14(1), 2199.

• Prystawski, B., Thibodeau, P., Potts, C., & Goodman, N. D. (2023). Psychologically-informed chain-of-thought prompts for metaphor understanding in large language models. Proceedings of the 45th Annual Conference of the Cognitive Science Society (CogSci 2023).

• Prystawski, B., Arumugam, D., & Goodman, N. D. (2023). Cultural reinforcement learning: a framework for modeling cumulative culture on a limited channel. Proceedings of the 45th Annual Conference of the Cognitive Science Society (CogSci 2023).

• Srivastava, M., Goodman, N. D., & Sadigh, D. (2023). Generating Language Corrections for Teaching Physical Control Tasks. 40th International Conference on Machine Learning (ICML).

• Yu, D., Goodman, N. D., & Mu, J. (2023). Characterizing tradeoffs between teaching via language and demonstrations in multi-agent systems. Proceedings of the 45th Annual Conference of the Cognitive Science Society (CogSci 2023).

• Prystawski, B., Li, M. Y., & Goodman, N. D. (2023). Why think step-by-step? Reasoning emerges from the locality of experience. Advances in Neural Information Processing Systems, Oral.

• Gandhi, K., Sadigh, D., & Goodman, N. D. (2023). Strategic Reasoning with Language Models. NeurIPS Workshop on Foundation Models and Decision Making.

• Gandhi, K., Fränken, J.-P., Gerstenberg, T., & Goodman, N. D. (2023). Understanding social reasoning in language models with language models. Advances in Neural Information Processing Systems (Spotlight).

• Tsvilodub, P., Franke, M., Hawkins, R. D., & Goodman, N. D. (2023). Overinformative Question Answering by Humans and Machines. Proceedings of the 45th Annual Conference of the Cognitive Science Society (2023).

• Mu, J., Li, X. L., & Goodman, N. (2023). Learning to compress prompts with gist tokens. Advances in Neural Information Processing Systems.

• He-Yueya, J., Poesia, G., Wang, R. E., & Goodman, N. D. (2023). Solving math word problems by combining language models with symbolic solvers. NeurIPS Workshop on Math-AI.

• Bayrooti, J., Goodman, N., & Tamkin, A. (2023). Multispectral Self-Supervised Learning with Viewmaker Networks. CVPR-PBVS 2023.

• Zelikman, E., Huang, Q., Poesia, G., Goodman, N. D., & Haber, N. (2023). Parsel: A (de-) compositional framework for algorithmic reasoning with language models. Advances in Neural Information Processing Systems (Spotlight).

• Tamkin, A., Glasgow, M., He, X., & Goodman, N. (2023). Feature Dropout: Revisiting the Role of Augmentations in Contrastive Learning. Advances in Neural Information Processing Systems.

• Wu, Z., Geiger, A., Potts, C., & Goodman, N. D. (2023). Interpretability at Scale: Identifying Causal Mechanisms in Alpaca. Advances in Neural Information Processing Systems.

• Wang, R. E., Wirawarn, P., Goodman, N., & Demszky, D. (2023). Sight: A large annotated dataset on student insights gathered from higher education transcripts. BEA 2023.

• Fränken, J.-P., Kwok, S., Ye, P., Gandhi, K., Arumugam, D., Moore, J., Tamkin, A., Gerstenberg, T., & Goodman, N. D. (2023). Social Contract AI: Aligning AI Assistants with Implicit Group Norms. NeurIPS Workshop in Socially Respobsible Language Modeling Research, Oral.

• Hsu, J., Poesia, G., Wu, J., & Goodman, N. (2023). Can Visual Scratchpads With Diagrammatic Abstractions Augment LLM Reasoning? NeurIPS Workshop on MathAI, and NeurIPS Workshop on I Can’t Believe It’s Not Better (ICBINB), 21–28.

• 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. Advances in 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 Physical and 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 of Motor 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.

• Wang, 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 across development. 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 for Contrastive 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 Conference on 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 clarification question generation without question examples. Proceedings of the 2021 Conference on Empirical Methods on Natural Language Processing (EMNLP).

• Wang, R., White, J., Mu, J., & Goodman, N. (2021). Calibrate your listeners! Robust communication-based 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 model of 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 agents lead 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 and Learning. 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 from partners to populations: Hierarchical inference supports convention formation on networks. Proceedings of the 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 for Multiscale 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.

• Yoon, E. J., Tessler, M. H., Goodman, N. D., & Frank, M. C. (2020). Polite speech emerges from competing 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.

• Obermeyer, F., Bingham, E., Jankowiak, M., Pradhan, N., Chiu, J., Rush, A. M., & Goodman, N. D. (2019-Preprint2019-Preprint). Tensor Variable Elimination for Plated Factor Graphs. ICML.

• 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 thousand studies 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.

• Degen, J., Trotzke, A., Scontras, G., Wittenberg, E., & Goodman, N. D. (2019). Definitely, maybe: A new experimental 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 and Conventions. 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 Meeting of the Association for Computational Linguistics, 619–628.

• 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: Learning and 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 computational modeling 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 product sustainability 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 from Dynamic 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: Lexical conventions 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.

• 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 response model 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 class understanding 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 resource-rational anchoring and adjustment. Psychonomic Bulletin and Review.

• Lieder, F., Griffiths, T. L., Huys, Q. J. M., & Goodman, N. D. (2017). The anchoring bias reflects rational use 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.

• 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 on theory 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 of abstraction 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)

• 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. [Best paper 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 of analysis 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 of teacher’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 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 the Last Word: Recency biases in early verbal behavior. Proceedings of the Thirty-Seventh Annual Conference of 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 in experimental 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 as communication. 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.

• Ullman, T. D., Stuhlmüller, A., Goodman, N. D., & Tenenbaum, J. B. (2014). Learning physics from dynamical 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 basis of 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. Proceedings of 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 Conference of the Cognitive Science Society.

• Gerstenberg, T., & Goodman, N. D. (2012). Ping pong in Church: Productive use of concepts in human probabilistic 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 alternative utterances 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 of psychological reasoning for human learning. Perspectives on Psychological Science, 7(4), 341–351.

• 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 the language 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 Conference on Artificial Intelligence and Statistics, 770–778.

• O’donnell, T. J., Snedeker, J., Tenenbaum, J. B., & Goodman, N. D. (2011). Productivity and reuse in language. Proceedings of the Thirty-Third Annual Conference of the Cognitive Science Society. [Winner of 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 of Probabilistic 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 in preschoolers’ exploratory play. Cognition, 120(3), 341–349.

• Ullman, T., Baker, C. L., Macindoe, O., Evans, O., Goodman, N. D., & Tenenbaum, J. B. (2010). Help or hinder: Bayesian models of social goal inference. Advances in Neural Information Processing Systems (NIPS 2010).

• 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 pedagogical situations. 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 and Dynamics 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 to model 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. Proceedings of 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 of Rule-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: a language for generative models. Uncertainty in Artificial Intelligence.

• Shafto, P., & Goodman, N. D. (2008). Teaching Games: Statistical Sampling Assumptions for Learning in 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 the Acquisition 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 Correlation from 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 the evidence: 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 the 1007 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. Advances in 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. Proceedings of 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 of rule-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: a Bayesian 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 Edition. Wiley-Blackwell.

• Goodman, N. D., Tenenbaum, J. B., & Gerstenberg, T. (2015). Concepts in a probabilistic language of thought. 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.), The probabilistic 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)

Software

• Webchurch, 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.

• “Some Glimpse AGI in ChatGPT. Others Call It a Mirage.” Wired, April 23, 2023.

• Newsday on the BBC World Service. May 1, 2023.

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.

• 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.

• 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.

• 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.

• 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 understanding social 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.

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.

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

• 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, Winter 2014.

• “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 in Experience (and Vice Versa)”, MIT, Spring 2008.

• Experience teaching mathematics at all levels, 1997-2005. (Details by request.)

Advising

Ph.D. students

• Kanishk Gandhi (Stanford, Computer Science), 2021-

• Eric Zelikman (Stanford, Computer Science), 2021-

• Ben Prystawski (Stanford, Psychology) 2021-

• Rose Wang (Stanford, Computer Science), 2020-

• Gabriel Poesia (Stanford, Computer Science), 2019-

• Julia White (Stanford, Electrical Engineering), 2018-2023 (anticipated)

• Alex Tamkin (Stanford, Computer Science), 2018-2023 (anticipated)

• Jesse Mu (Stanford, Computer Science), 2018-2023 (anticipated)

• Mike Wu (Stanford, Computer Science), 2017-2022 (Now Founder at web3analytic)

• Erin Bennett (Stanford, Psychology), 2015-2020 (Now Computer Science & Mathematics Teacher at Kehillah Jewish High School)

• Robert X. D. Hawkins (Stanford, Psychology), 2014-2019 (Now Assistant Professor at University of Wisconsin)

• Michael Henry Tessler (Stanford, Psychology), 2013-2018 (Now Research Scientist at DeepMind)

• Desmond Ong (Stanford, Psychology), 2012-2017 (Now Assistant Professor of Psychology at UT Austin)

• Daniel Ritchie (Stanford, Computer Science), 2011-2016 (Now Assistant Professor of CS at Brown University)

• Justine Kao (Stanford, Psychology), 2011-2016 (Now Data Science Manager at FAIR)

• Long Ouyang (Stanford, Psychology), 2010-2015 (Now Research Scientist at OpenAI)

• Daniel Hawthorne (Stanford, Psychology), 2010-2015 (Now Research Data Scientist at Meta)

• Andreas Stuhmueller (MIT, BCS), completed 2015 (Now CEO Ought, Inc.)

Post-doctoral students

• Judy Fan, 2017-2019 (Now Assistant Professor of Psychology, UCSD/Stanford)

• Leon Bergen, 2016-2017 (Now Assistant Professor of Linguistics, UCSD)

• Judith Degen, 2013-2017 (Now Assistant Professor of Linguistics, Stanford)

• Gregory Scontras, 2014-2016 (Now Associate Professor of Linguistics, UC Irvine)

• Siddarth Narayanaswami 2013-15 (Now Reader, University of Edinburgh)

• Daniel Ly, 2013-15 (Now Associate Director at Proof Diagnostics, Inc)

• Daniel Lassiter, 2011-13 (Now Senior Lecturer, University of Edinburgh)

• Joseph Austerweil, 2013 (Now Associate Professor of Cognitive Science, University of Wisconsin)

Ph.D. Committees

• Chenlin Meng (Stanford, CS)

• Veronica Boyce (Stanford, Psychology)

• Sarah Wu (Stanford, Psychology)

• Alex Durango (Stanford, Neuroscience)

• David Rose (Stanford, Psychology)

• Yunsung Kim (Stanford, CS)

• 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 2013

• 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, 2021-2022, 2022-2023.

• Committee on Review of Undergraduate Majors, 2021-22, 2022-23. (2023-24, Chair.)

• 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.

Miscellaneous:

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