I am currently employed as a post-doctoral fellow at 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.
My research program is focused on the Behavioral Science of Big Data - how new datasets and algorithms are changing our daily life, and expanding the researcher toolbox. Across several domains - conversations, recommendations, and online education - I have explored the ways that big data can help to uncover the roots of our decision-making, while nurturing and guiding our behavior outside the lab. Increasingly, our judgments and decisions are a matter of record. Our behavior is tracked, and fed into new algorithms designed to predict, understand, and assist people and businesses in reaching their goals. My research sits at the intersection of this developing sciences of machine learning and behavioral science.
In particular, my research primarily applies machine learning to 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 to pursue our goals. In these cases, our perspective-taking capacity can be limited, in part because the information available about someone - their words, their actions - are so complex and high-dimensional. My research focuses on examples where machine learning can help to understand and to benchmark the choices that people make while interacting with one another.