In my research, I develop and explore applications for machine learning in behavioral science. In particular, I primarily focus on cases where we know human judgment is biased or incomplete. Many of these examples are “interpersonal prediction problems” - domains where we must understand other people’s preferences and intentions and navigate complex social interactions. Often we find that our perspective-taking capacity can be limited or biased. But in these cases, machine learning can help to understand and improve on the choices that people make.

Currently, I am pursuing this research as a post-doctoral fellow at Massachussetts Institute of Technology and Harvard University. I completed my undergraduate degree in Psychology at the University of Toronto and in 2014, I completed a PhD and an MBA in Behavioral Science at the University of Chicago Booth School of Business.