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. Some specific questions and representative publications appear on my departmental webpage. These interests sometimes lead me into other areas of research: I have recently been exploring some ideas in nonparametric Bayesian statistics and formal models of cultural evolution.
If you are interested in learning about using Bayesian methods to model cognition, you might find my reading list on Bayesian methods useful. You could also check the foundations section of the lab publication list, which contains overviews and tutorials.