About Me

I'm an Assistant Professor in the Faculty of Computing and Data Sciences at Boston University. Before starting at BU, I completed my PhD at UC Berkeley as a member of the Berkeley Artificial Intelligence Research Lab studying the intersection of machine learning and cognitive science. I continued this work as a postdoc at Princeton University in the Department of Computer Science. My recent work explores how machine learning can be used to automate the search for interpretable, high-precision models of human behavior.

Featured Publications

Capturing the Complexity of Human Strategic Decision-Making with Machine Learning

Zhu Jian-Qiao, Joshua Peterson, Benjamin Enke, Thomas Griffiths (In Revision)

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Deep models of superficial face judgments

Joshua Peterson, Stefan Uddenberg, Thomas Griffiths, Alexander Todorov, Jordan Suchow (2022)

Proceedings of the National Academy of Sciences (PNAS)

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Using large-scale experiments and machine learning to discover theories of human decision-making

Joshua Peterson, David Bourgin, Mayank Agrawal, Daniel Reichman, Thomas Griffiths (2021)

Science

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Capturing human categorization of natural images by combining deep networks and cognitive models

Ruairidh Battleday*, Joshua Peterson*, Thomas Griffiths (2020)

Nature Communications

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* equal contribution

Scaling up psychology via Scientific Regret Minimization

Mayank Agrawal, Joshua Peterson, Thomas Griffiths (2020)

Proceedings of the National Academy of Sciences (PNAS)

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Featured Conference Papers

Human uncertainty makes classification more robust

Joshua Peterson*, Ruairidh Battleday*, Thomas Griffiths (2019)

IEEE International Conference on Computer Vision (ICCV)

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* equal contribution

Cognitive model priors for predicting human decisions

David Bourgin*, Joshua Peterson*, Daniel Reichman, Thomas Griffiths, Stuart Russell (2019)

International Conference on Machine Learning (ICML)

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* equal contribution