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
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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.
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Post-Doctoral Associate, Massachusetts Institute of Technology, 2005-2008.
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Lecturer, St. Edwards University, 2004-2005.
Education
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Ph.D., Mathematics, University of Texas at Austin, 2003.
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B.S. Physics, Cum Laude, University of Arizona, 1997.
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B.A. Mathematics, Cum Laude, University of Arizona, 1997.
Honors
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Best paper award International Conference on Educational Data Mining, 2020 (EDM 2020).
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2020 Cognitive Science Society paper prize for computational modeling of language.
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AAAI best student paper award, 2019.
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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.
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2016 Alfred P. Sloan Research Fellow in Neuroscience.
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2015 Cognitive Science Society paper prize for applied computational modeling.
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2014 Cognitive Science Society paper prize for computational modeling of language.
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Roger N. Shepard Distinguished Visiting Scholar, 2013-14, University of Arizona.
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John Philip Coghlan Fellow, 2013-14 and 2014-15.
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2012 Cognitive Science Society paper prize for computational modeling of language.
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2011 International Joint Conference on Artificial Intelligence best poster prize.
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2011 Cognitive Science Society paper prize for computational modeling of language.
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2010 J. S. McDonnell Foundation Scholar Award.
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2007 Cognitive Science Society paper prize for computational modeling of higher-level cognition.
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2007 Cognitive Science Society paper prize for computational modeling of perception and action.
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NSF VIGRE Fellowship, 2001-2002.
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University of Texas Continuing Graduate Study Fellowship, 2001-2002.
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Bruton Graduate Fellowship, 2000.
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National Merit Scholarship, 1994-1997.
Grants
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AI Tutors to Help Prepare Students for the 21st Century Workforce, Stanford HAI Hoffman-Yee Grant, 2020.
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Toward Grounded, Adaptive Communication Agents, Stanford HAI Hoffman-Yee Grant, 2020.
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Expeditions: Collaborative Research: Understanding the World Through Code, NSF, 2020 - 2025.
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MURI: Visual Commonsense, 2019-2021 (Sub-award from UCLA).
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Homo SocioNeticus: Scaling the cognitive foundations of online social behavior, 2018 - 2021 (Sub-award from Virginia Tech).
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Alfred P. Sloan Research Fellow in Neuroscience, 2016 - 2019.
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Amortized Inference for Probabilistic Programs, DARPA, Oct 2013 - Jul 2017.
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Grounding Lexical Meaning in Core Cognition, ONR, Sep 2013 - Mar 2017.
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Development of probmods.org web-book, Stanford VPOL, 2013.
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Grounded language understanding as social cognition, ONR, Jan 2013 - Jan 2016 (PI: Potts).
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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).
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J. S. McDonnell Foundation Scholar Award, Oct 2010 - Oct 2016.
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A Framework for Core Cognition, ONR, Jul 2009 - Dec 2012 (PI: Tenenbaum).
Publications
Peer-reviewed Journal Articles
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.
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.
Buch, S., Fei-Fei, L., & Goodman, N. (2021). Neural Event Semantics for Grounded Language Understanding. Transactions of the Association for Computational Linguistics (TACL).
Gerstenberg, T., Goodman, N., Lagnado, D., & Tenenbaum, J. (2021). A counterfactual simulation model of causal judgments for physical events. Psychological Review.
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.
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., Frank, M. C., & Goodman, N. D. (2020). Characterizing the dynamics of learning in repeated reference games. Cognitive Science, 44(6), e12845.
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.
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.
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.
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.
Ong, D., Soh, H., Zaki, J., & Goodman, N. (2019). Applying Probabilistic Programming to Affective Computing. IEEE Transactions on Affective Computing.
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.
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
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.
Ong, D. C., Goodman, N. D., & Zaki, J. (2018). Happier than thou? A self-enhancement bias in emotion attribution. Emotion, 18(1), 116.
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).
Scontras, G., Degen, J., & Goodman, N. D. (2017). Subjectivity predicts adjective ordering preferences. Open Mind.
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.
Ballard, I., Miller, E., Piantadosi, S., Goodman, N., & McClure, S. (2017). Beyond Reward Prediction Errors: Human Striatum Updates Rule Values During Learning. Cerebral Cortex.
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.
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.
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.
Ong, D. C., Zaki, J., & Goodman, N. D. (2015). Affective Cognition: Exploring lay theories of emotion. Cognition, 143, 141–162.
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.
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.
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.
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.
Goodman, N. D., & Stuhlmüller, A. (2013). Knowledge and implicature: Modeling language understanding as social cognition. Topics in Cognitive Science, 5, 173–184.
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.
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.
Frank, M. C., & Goodman, N. D. (2012). Predicting pragmatic reasoning in language games. Science, 336(6084), 998–998.
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.
Goodman, N. D., Ullman, T. D., & Tenenbaum, J. B. (2011). Learning a theory of causality. Psychological Review, 118(1), 110.
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.
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.
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., & Tenenbaum, J. B. (2009). Using speakers’ referential intentions to model early cross-situational word learning. Psychological Science, 20(5), 578–585.
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.
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
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Giroux, E. & Goodman, N. D. (2006). On the stable equivalence of open books in three-manifolds. Geometry & Topology.
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Goodman, N. D. (2005). Overtwisted open books from sobering arcs. Algebraic and Geometric Topology.
Peer-reviewed 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. 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.
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).
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).
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.
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.]
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.]
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.
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.]
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.
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
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.]
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.
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.
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.
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.
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.
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.
Ritchie, D., Stuhlmüller, A., & Goodman, N. D. (2016). C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching. AISTATS 2016.
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).
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.]
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.
Luong, T., O’Donnell, T., & Goodman, N. D. (2015). Evaluating Models of Computation and Storage in Human Sentence Processing. CogACLL 2015.
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.
Yang, L., Hanrahan, P., & Goodman, N. D. (2014). Generating Efficient MCMC Kernels from Probabilistic Programs. AISTATS 2014.
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.
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.
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).
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.]
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.
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).
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.
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.
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]
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.
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.
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., 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.
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.
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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.
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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
)
Technical Reports
Chen, J. P., Obermeyer, F., Lyapunov, V., Gueguen, L., & Goodman, N. D. (2018). Joint Mapping and Calibration 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 with probabilistic 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).
Software
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Webchurch, MIT-Church, Bher, Cosh. Implementations of the Church probabilistic programming language.
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WebPPL. A javascript-based probabilistic programming language.
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Pyro. A deep probabilistic programming language based on Python and PyTorch.
Popular Press (selected articles)
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“A grand unified theory of AI,” MIT News, March 30, 2010.
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“I, algorithm.” New Scientist, January 29, 2011.
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“More Than Child’s Play: Ability to Think Scientifically Declines as Kids Grow Up.” Scientific American, September 21, 2011.
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“Artificial Intelligence Could Be on Brink of Passing Turing Test.” WIRED, April 12, 2012.
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“Context is key to making computers better conversationalists.” WIRED.uk, June 20, 2012.
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“Forget the Turing Test: Here’s How We Could Actually Measure AI.” WIRED, June 12, 2014.
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“Solve For Standing Ovation: Should AI Researchers Bother Building A TED-Bot?” Popular Science, March 28, 2014.
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“This Computer Knows When ‘Literally’ Isn’t Literal.” Discover, August 5, 2014.
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“Why Can’t Robots Understand Sarcasm?” The Atlantic, January 22, 2015.
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“Think you’re punny? Computer that can tell how good a joke is.” New Scientist, August 12, 2015.
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“Call it Clement Droid: a machine that has a droll sense of humour.” The Times, August 15, 2015.
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“What people can learn from algorithms — and algorithms can learn from people.” Boston Globe, May 27, 2016.
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“AI’s Langauge Problem.” MIT Technology Review, August 9, 2016.
Invited Presentations
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AAAI Symposium “Conceptual Abstraction and Analogy in Natural and Artificial Intelligence”, November 2020.
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MPI Tübingen, November 2020.
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Brown CLIPS “Social Cognitive Seminar”, October 2020.
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“Optimal experiment design” workshop, Mathematical Psychology, July 2020.
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University of Saarbrucken, Germany, February 2020.
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ICLR, New Orleans, LA, May 2019.
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Open Source Leadership Summit, Half Moon Bay, CA, March 2019.
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College de France, Paris, France, February 2019.
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“Robots Seminar”, Oxford, UK, February 2019.
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Deepmind, London, UK, February 2019.
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Probabilistic Programming, Cambridge, MA, October 2018.
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KogWis (Germany Cognitive Science Society Biannual Conference), Darmstadt, Germany, September 2018.
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“Program Induction” workshop, CogSci, Madison, WI, July 2018.
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A-Star, Singapore, June 2018.
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Dubrovnik Cognitive Science Conference, Dubrovnik, Croatia, May 2018.
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Cognitive Science Colloquium, Johns Hopkins University, Baltimore, MD, March 2018.
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“Emergent Language” workshop, NIPS, Long Beach, CA, November 2017.
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“Combining Academia and Industry Workshop”, CogSci, London, UK, July 2017.
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Gatsby Unit, London, UK, July 2017.
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PLEMM workshop, Facebook, Menlo Park, CA, May 2017.
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EmTech Digital, San Francisco, CA, March 2017.
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Uber Technology Conference, Uber, Palo Alto, CA, February 2017.
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Machine Learning Seminar, University of Washington, Seattle, WA, January 2017.
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Tokyo, Japan, December 2016.
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Society for Philosophy and Psychology, Austin, TX, May 2016.
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Adobe Machine Learning Seminar, San Jose, CA, April 2016.
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DGfS workshop on Computational Pragmatics, Konstanz, Germany, February 2016.
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SPLAP Workshop, UCSC, Santa Cruz, CA, February 2016.
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Linguistic Universals Colloquium, Harvard University, Cambridge, MA, October 2015.
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CS, Brown University, Providence, RI, October 2015.
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CLPS Colloquium, Brown University, Providence, RI, October 2015.
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ILLC Colloquium, University of Edinburgh, Edinburgh, UK, September 2015.
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XPRAG 7, Plenary Speaker, Chicago, IL, July 2015.
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Microsoft Faculty Summit, Seattle, WA, July 2015.
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AAAI symposium on Knowledge Representation and Reasoning, Stanford, CA, March 2015.
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UCSD seminar on Computational and Experimental Pragmatics, San Diego, CA, February 2015.
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Princeton Cognitive Science Colloquium, Princeton, NJ, January 2015.
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University of Maryland Cognitive Science Colloquium, College Park, MD, January 2015.
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University of Maryland NLP Seminar, College Park, MD, January 2015.
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Northwestern University Linguistics Colloquium, Evanston, IL, January 2015.
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Cognitive Science Society invited symposium “Foundations of Social Cognition”, Quebec City, Canada, July 2014.
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NYU Psychology, New York, April 2014.
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DE Shaw Tech Talk, New York, April 2014.
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University of Arizona Cognitive Science Colloquium, Tucson, AZ, February 2014.
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AI Colloquium, Groningen, Netherlands, February 2014.
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Amsterdam Colloquium, Amsterdam, Netherlands, December 2013.
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NeuroSpin, Paris, France, December 2013.
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IIIS Machine Learning Seminar, Tsinghua University, Beijing, China, October 2013.
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“Logic across the university” workshop, Tsinghua University, Beijing, China, October 2013.
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Intelligence Initiative Seminar, MIT, Cambridge, MA, September 2013.
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Laboratory for Developmental Science Seminar, Harvard, Cambridge, MA, September 2013.
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CogSci workshop “Producing Referring Expressions”, Berlin, Germany, August 2013.
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CogSci workshop “Motivations and Goals in Developing Integrative Models of Human Cognition”, Berlin, Germany, August 2013.
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“Rational Choice Workshop”, Dept. of Economics, University of Chicago, Chicago, IL, May 2013.
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UT-Austin Linguistics Colloquium, Austin, TX, April 2013.
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UT-Austin Cognitive Systems Forum, Austin, TX, April, 2013.
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Google, Mountain View, CA, April 2013.
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Intel, Sunnyvale, CA, April 2013.
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IMBS workshop “Quantum thinking”, Irvine, CA, February 2013.
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Keynote, Principles of Programming Languages (POPL 13), Rome, Italy, January 2013.
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Linguistics Colloquium, Tubingen, Germany, January 2013.
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Invited Symposium, Budapest CEU Conference on Cognitive Development, Budapest, Hungary, January 2013.
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Indiana Cognitive Science Colloquium, Bloomington, IN, November 2012.
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Statistical Relational Artificial Intelligence workshop, UAI, Avalon, CA, August 2012.
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Early Career Keynote Speaker, International Conference on Thinking, symposium on Causal Learning & Reasoning, London 2012.
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Early Career Keynote Speaker, International Conference on Thinking, symposium on Inductive Reasoning, London 2012.
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Reasoning and Interaction workshop, UT-Austin, Austin, TX, June 2012.
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Workshop “Interdisciplinary approaches to implicature.” MIT, Cambridge, MA, May 2012.
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California Cognitive Science Conference, Berkeley, CA, April 2012.
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International Congress on Computer Vision, Vision Grammars Workshop, Barcelona, Spain, November 2011.
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UC Berkeley Institute for Human Development seminar, Berkeley, CA, October 2011.
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UC Berkeley Institute for Cognitive and Brain Sciences seminar, Berkeley, CA, September 2011.
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Gatsby Unit special seminar, University College, London, UK, September 2011.
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London Judgment and Decision Making seminar, London, UK, September 2011.
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AAAI workshop on Plan and Intent Recognition, San Francisco, CA, August 2011.
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UC Merced Cognitive and Information Sciences Colloquium, Merced, CA, March 2011.
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UCSC Psychology Colloquium, Santa Cruz, CA, February 2011.
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Neural Information Processing Systems workshop “Modeling human communication dynamics”. Whistler, BC, December 2010.
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SRI, Menlo Park, CA, November 2010.
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UCSD Psychology Colloquium, San Diego, CA, November 2010.
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CSLI Symposium, Stanford University, Stanford, CA, October 2010.
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Humanity+ Summit, Harvard University, Cambridge, MA, June 2010.
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Cornell Workshop on Grammar Induction, Ithaca, NY, May 2010.
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Massachusetts General Hospital, Biostatistics Seminar. Boston, MA, March 2010.
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Johns Hopkins University, Psychology Department special seminar. Baltimore, MD, January 2010.
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Stanford University, Psychology Department special seminar. Stanford, CA, January 2010.
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University of Rochester, Brain and Cognitive Sciences colloquium. Rochester, NY, October 2009.
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University of Michigan, Developmental Psychology Brown Bag seminar. Ann Arbor, MI, October 2009.
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Brown University, Pattern Theory seminar. Providence, RI, October 2009.
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University of Edinburgh, Informatics Division colloquium. Edinburgh, UK, July 2009.
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Banff International Research Station workshop “Probabilistic models of cognitive development”. Banff, BC, May 2009.
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Invited commentary, Interdisciplinary Graduate Conference on Consciousness. Boston, MA, April 2009.
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MIT, Brain and Cognitive Sciences special seminar. Boston, MA, March 2009.
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Neural Information Processing Systems workshop “Probabilistic programming”, Whistler, BC, December 2008.
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Neural Information Processing Systems workshop “Human learning meets machine learning”. Whistler, BC, December 2008.
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New York University, Developmental Psychology seminar. New York, NY, October 2008.
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Keynote speaker, International Conference on Inductive Logic Programming. Prague, September 2008.
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University of Texas, Cognitive Psychology seminar. Austin, TX, August 2008.
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Center for Advanced Study in the Behavioral Sciences workshop “Early mechanisms of understanding social causation” (Festschrift for John S. Watson). Stanford, CA, April 2008.
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International Conference on Infant Studies invited symposium “From statistical regularities to conceptual inference”. Vancouver, BC, March 2008.
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ONR Workshop on Computational Social Cognition. MIT, Cambridge, MA, March 2008.
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Harvard university, Psychology colloquium. Cambridge, MA, February 2008.
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University of California, Berkeley, Computational Cognitive Science seminar. Berkeley, CA, November 2007.
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AAAI Fall Symposia workshop “Representation Change”. Washington, DC, November 2007.
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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”.)
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McDonnell Foundation Workshop on Moral Cognition. Pasadena, CA, May 2007.
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University of Salzburg, Institute fur Psychologie colloquium. Salzburg, AU, April 2007.
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University of Gottingen, Cognitive and Decision Sciences seminar. Gottingen, GM, April 2007.
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Rutgers University, Center for Cognitive Science seminar. Piscataway, NJ, March 2007.
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Society for Philosophy and Psychology, Invited symposium on Causality. St. Louis, MO, June 2006.
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University of California, Berkeley, Cognitive Development seminar. Berkeley, CA, 2006.
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University of Michigan, Developmental Psychology seminar. Ann Arbor, MI, 2006.
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Brown University, Cognitive Science seminar. Providence, RI, 2005.
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M.I.T., Computational Cognitive Science seminar. Cambridge, MA, December 2004.
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Bryn Mawr College, Contact Topology seminar. Bryn Mawr, PA, April 2003.
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University of Pennsylvania, Department of Mathematics Geometry-Topology seminar. Philadelphia, PA, January 2003.
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University of Texas at Austin, Department of Mathematics Topology seminar. Austin, TX, March 2002.
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Columbia University, Department of Mathematics Topology seminar. New York, NY, March 2001.
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State University of New York, Department of Mathematics Geometry seminar. Stony Brook, NY, March 2001.
Professional Services
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Journal Reviewer: 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.
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Conference Proceedings Reviewer (selected): Cognitive Science. Neural Information Processing Systems. Society for Philosophy and Psychology. Uncertainty in Artificial Intelligence.
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Grant reviewer: NSF (ad-hoc and panelist), ONR.
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Program committee / Senior PC: Cognitive Science Society, 2014, 2015, 2016. AAAI 2016. AIStats 2017.
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Co-organizer: Cognitive Science Society, symposium “Empirical and Computational Approaches to Metaphor and Figurative Meaning”, Pennsylvania, August 2016.
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Co-organizer: Cognitive Science Society, symposium “The Emergence of Conventions”, Pennsylvania, August 2016.
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Co-Organizer: European Summer School of Logic Language and Information, workshop “Formal and Experimental Pragmatics”. Tubingen, August 2014.
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Organizer: Stanford Pragmatics and Social Cognition Workshop, March 2013.
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Co-Organizer: NIPS workshop on Probabilistic Programming, December 2012.
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Area chair: NIPS 2011. Granada, Spain, December 2011.
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Co-Organizer: IPAM summer school “Probabilistic Models of Cognition”. Los Angeles, CA, July 2011.
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Organizer: workshop “Probabilistic Programming in AI”. Los Altos, CA, January 2010.
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Co-Organizer: NIPS workshop “Bounded-rational analyses of human cognition: Bayesian models, approximate inference, and the brain”. Whistler, BC, December 2009.
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Program committee: International Workshop on Statistical Relational Learning. Leuven, Belgium, July 2009.
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Co-Organizer: Cognitive Science Society, workshop “Intuitive pedagogical reasoning: an interdisciplinary workshop”. Amsterdam, Netherlands, August 2009.
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Co-Organizer: Annual Summer Interdisciplinary Conference, symposium “Bayesian models in psychology”. Valle ‘Aosta, Italy, July 2009.
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Co-Organizer: Eastern Psychology Association, workshop “Social routes to causal knowledge: action, imitation, and pedagogy”. Boston, MA, March 2008.
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Co-Organizer: McDonnell Foundation workshop “Explanation and prior knowledge”. Cambridge, MA, November 2006.
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Mentor (1999-2001), Canada/USA Mathcamp.
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Co-Organizer and Coordinator of Hiring, Canada/USA Mathcamp 2000, 2001.
Outreach
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Guest lecture, Summer School in Cognitive Science, Montreal, Canada, June 2016.
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Guest lecturer, SAILORS program, Stanford, 2015, 2016, 2017.
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Studium Generale, Groningen, Netherlands, February 2014.
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Visiting Lecturer (2006, 2008, 2010, 2011, 2014), Canada/USA Mathcamp.
Teaching
University Courses
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“Langauge and Thought” (OSPMADRD 19). Stanford (BOSP Madrid). Winter 2020.
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“Independent Study in Machine Translation” (OSPMADRD 20). Stanford (BOSP Madrid). Winter 2020.
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“Psychometrics and automated experiment design” (PSYCH 241). Stanford. Autumn 2019.
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“Langauge and Thought” (Psych 132). Stanford. Spring 2019.
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“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.
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“Topics in Natural and Artificial Intelligence”. Stanford. Autumn 2018.
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“Seminar on the Science of Meditation” (Psych 295). Stanford. Spring 2018.
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“Seminar in Semantics: Formal semantics and the psychology of reasoning” (LINGUIST 236/PSYCH 236). Stanford. Spring 2017.
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“Foundations of Cognition” (Psych 205). Stanford. Spring 2015.
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“Probabilistic Models of Social Behavior and Affect” (Psych 241). Stanford. Spring 2014. (Co-taught with J. Zaki, M. Frank.)
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“Representations of Meaning” (Psych 236 c, Linguist 236). Stanford. Spring 2013.(Co-taught with C. Potts.)
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“Introduction to Cognitive Science” (SymSys 100, Psych 34). Stanford. Spring 2012, Winter 2013, Winter 2014.
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“Formal and Computational Approaches in Psychology and Cognitive Science” (Psych 239). Stanford. Spring 2011. (Co-taught with J. McClelland.)
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Co-taught (with L. Schulz and C. Moore): “Perception, Conception, and Action: Grounding Thoughts in Experience (and Vice Versa)”, MIT, Spring 2008.
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Experience teaching mathematics at all levels, 1997-2005. (Details by request.)
Summer Schools and Tutorials
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Probabilistic Programming for Advanced Machine Learning (summer school for DARPA program of the same name), Portland, August 2016.
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“Probabilistic programming languages.” European Summer School of Logic Language and Information. Tubingen, August 2014.
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“Probability in semantics and pragmatics” (with D. Lassiter). European Summer School of Logic Language and Information. Dusseldorf, August 2013.
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“Stochastic lambda calculus and its applications in cognitive science.” (Invited course.) North-American Summer School of Logic, Language, and Information. Austin, TX, June 2012.
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“Computational Cognitive Science: Probability, Programs, and the Mind.” European Summer School of Logic Language and Information. Copenhagen, August 2010.
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Tutorial on probabilistic models of cognition (with T. O’Donnell). Cornell University, October 2009.
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IPAM Graduate Summer School: “Probabilistic Models of Cognition: The Mathematics of Mind”, Los Angeles CA, July 2007. ( 3 lectures.)
Advising
Post-doctoral students
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Judy Fan, 2017-2019 (Now assistant professor of Psychology, UCSD)
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Daniel Ritchie, 2016-17 (Now assistant professor of CS, Brown)
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Leon Bergen, 2016-2017 (Now assistant professor of Linguistics, UCSD)
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Judith Degen, 2013-2017 (Now assistant professor of Linguistics, Stanford)
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Andreas Stuhmueller, 2015-2017 (Now CEO Ought, Inc.)
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Long Ouyang, 2015-2016
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Gregory Scontras, 2014-2016 (Now associate professor of Linguistics, UC Irvine)
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Siddarth Narayanaswami 2013-15 (Now lecturer, University of Edinburgh)
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Daniel Ly, 2013-15
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Daniel Lassiter, 2011-13 (Now assistant professor of Linguistics, Stanford University)
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Joseph Austerweil, 2013 (Now associate professor of Cognitive Science, University of Wisconsin)
Ph.D. students
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Gabriel Poesia (Stanford, Computer Science)
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Julia White (Stanford, Electrical Engineering)
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Alex Tamkin (Stanford, Computer Science)
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Jesse Mu (Stanford, Computer Science)
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Mike Wu (Stanford, Computer Science)
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Ben Peloquin (Stanford, Psychology), on leave
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Erin Bennett (Stanford, Psychology), completed 2020
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Robert X. D. Hawkins (Stanford, Psychology), completed 2019
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Michael Henry Tessler (Stanford, Psychology), completed 2018
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Desmond Ong (Stanford, Psychology), completed 2017
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Daniel Ritchie (Stanford, Computer Science), completed 2016
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Justine Kao (Stanford, Psychology), completed 2016
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Long Ouyang (Stanford, Psychology), completed 2015
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Daniel Hawthorne (Stanford, Psychology), completed 2015
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Andreas Stuhmueller (MIT, BCS), completed 2015
Ph.D. Committees
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Shao-Fang (Pam) Wang (Stanford, Psychology)
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Natalia Velez (Stanford, Psychology), completed 2019
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Andrew Lampinen (Stanford, Psychology), completed 2019
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Erica Yoon (Stanford, Psychology), completed 2019
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Ziang Xie (Stanford, CS), completed 2018
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Justin Johnson (Stanford, CS), completed 2018
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Will Munroe (Stanford, CS), completed 2018
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Steven Hansen (Stanford, Psychology), completed 2018
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Sida Wang (Stanford, CS), completed 2017
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Ian Ballard (Stanford, Neuroscience), completed 2017
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Molly Lewis (Stanford, Psychology), completed 2016
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Leon Bergen (MIT, BCS), completed 2015
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Lingfeng Yang (Stanford, Computer Science), completed 2015
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Rahul Sharma (Stanford, Computer Science), completed 2015
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Eric Schkufza (Stanford, Computer Science), completed 2015
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Eric Miller (Stanford, Psychology), completed 2015
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Tomer Ulman (MIT, BCS), completed 2014
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Spence Green (Stanford, Computer Science), completed 2014
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Ranjitha Kumar (Stanford, Computer Science), completed 2013
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Yi-Ting Yeh (Stanford, Computer Science), completed 2013
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Thomas Icard III (Stanford, Philosophy), completed 2013
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Jerry Talton III (Stanford, Computer Science), completed 2012
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Jeremy Glick (Stanford, Psychology), completed 2011
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Daniel Sternberg (Stanford, Psychology), completed 2011
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Steve Piantadosi (MIT, BCS), completed 2011
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Timothy J. O’Donnell (Harvard, Psychology), completed 2011
University Committees
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Psychology Department, Graduate Admissions Committee (Chair), 2018-19, 2019-20, 2020-2021.
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CS Department, Pre-Hiring Committee, 2020-21.
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Human-centered AI Initiative Design Committee, 2018-19.
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CS Department, Curriculum Revision Committee, 2018-19.
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CS Department, Graduate Admissions Committee, 2018-19.
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CS Department, Ad-Hoc Promotion Committee, 2018-19.
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Psychology Department, Graduate Admissions Committee, 2016-17, 2017-18.
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CS Department, MA Advising Committee, 2017-18.
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Psychology Department, Colloquium Committee, 2014-2015.
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Psychology Department, Cognitive Search Committee, 2014-2015.
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Psychology Department, Curriculum Committee, 2014-2015.
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Psychology Department, Colloquium Committee, 2013-2014.
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Human Subjects Research IRB, 2013-2014.
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Psychology Department, Colloquium Committee, 2012-2013.
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Psychology Department, Graduate Admissions Committee, 2012-2013.
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Psychology Department, Cognitive Search Committee, 2012-2013.
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Psychology Department, Computer Committee, 2012-2013.
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Psychology Department, Graduate Program Committee, 2011-2012.
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Psychology Department, Graduate Admissions Committee, 2011-2012.
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Psychology Department, Cognitive Search Committee, 2011-2012.
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Psychology Department, Computer Committee, 2011-2012.
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Psychology Department, Cognitive Search Committee, 2010-2011.
Miscellaneous:
Citizen of the USA.
Member Cognitive Science Society.
Member Psychonomic Society.