I'm currently a post-doc in the Psychology and Computer Science departments at Princeton University working with Tom Griffiths and Jonathan Cohen on resource-rational planning. Previously, I was also affiliated with the Learn and Verify Group in the Department of Electrical Engineering and Computer Sciences at UC Berkeley. I recieved my Ph.D. in Cognitive Science from Brown University, working with Joseph Austerweil and Fiery Cushman on teaching and social learning, and my Master's in Computer Science from Brown working with Michael Littman on human-in-the-loop reinforcement learning. As an undergraduate at Princeton, I majored in Philosophy and minored in Computer Science, and Gilbert Harman advised my senior thesis. I'm originally from New York City.
Feel free to reach out! My email is firstname.lastname@example.org
My CV can be found here.
I am interested in how people solve complex everyday problems, how they participate in rich social interactions, and how these interact and amplify one another. I am also interested in how to implement these abilities in machines. My collaborators and I study the computational principles and cognitive mechanisms that underpin human problem solving and social cognition by drawing on ideas and methods from several fields, including cognitive psychology, social psychology, artificial intelligence, statistics, neuroscience, and philosophy.
Much of my recent research focuses on understanding human planning and problem solving. For examples of this work, see this 2019 review paper on the value of abstraction, as well as this or this conference paper on meta-reasoning and resource-rational planning. My collaborators and I have also done work on teaching with rewards and punishments, teaching by demonstration, theory of mind, and learning abstract norms, among other topics in human social cognition and communication. You can find an updated list of publications here (including links to pdfs).
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