Professor of Psychology and Cognitive Science
I am interested in developing mathematical models of higher level cognition, and understanding the formal principles that underlie our ability to solve the computational problems we face in everyday life. My current focus is on inductive problems, such as probabilistic reasoning, learning causal relationships, acquiring and using language, and inferring the structure of categories. I try to analyze these aspects of human cognition by comparing human behavior to optimal or "rational" solutions to the underlying computational problems. For inductive problems, this usually means exploring how ideas from artificial intelligence, machine learning, and statistics (particularly Bayesian statistics) connect to human cognition. These interests sometimes lead me into other areas of research such as nonparametric Bayesian statistics and formal models of cultural evolution.
I am the Director of the Computational Cognitive Science Lab at Princeton University. Here is a reasonably up-to-date curriculum vitae.
My friend Brian Christian and I recently wrote a book together about the parallels between the everyday problems that arise in human lives and the problems faced by computers. Algorithms to Live By outlines practical solutions to those problems as well as a different way to think about rational decision-making.
I am interested in how novel approaches to data collection and analysis - particularly "big data" - can change psychological research. Read my manifesto and check out the Center for Data on the Mind.
My academic publications are available chronologically and by topic or from Google Scholar.