I am currently a research scientist (and recent PhD) at UC Berkeley under the advising of Tom Griffiths in the Computational Cognitive Science lab. My research lies at the intersection of machine learning and cognitive science where I'm interested in using computational models to formalize what it means to learn: an ability central to both artificial and biological intelligence. In particular, I explore ways in which people learn and reason with concepts and categories of things like colors and animals, and how this knowledge might be structured in semantic memory.

As a computational cognitive scientist, I follow a top-down approach to reverse-engineering human learning. This view holds that we can understand key aspects of cognition by identifying the problem being solved and comparing how people's behavior differs from an ideal observer. By generating a formal description of the problem being solved, we can develop experiments to test the types of mental representations and inductive biases people use in their everyday lives. As a result, this top-down approach provides us with a better understanding of human cognition and lets us build machine learning models that perform more like people.

Curriculum Vitae | Google Scholar