I’m a Ph.D. student in Statistics at Harvard University. My research develops and applies data science and statistical tools to complex social problems, such as mass incarceration, hiring discrimination, and other issues at the intersection of statistics, computer science, and policy. In particular, I’m interested in causal inference, computational social science, Bayesian statistics, machine learning, algorithmic fairness, and statistical computing. I’m a sometimes contributor to the Stan Bayesian data analysis ecosystem.

Before coming to Harvard, I was a Ph.D. student in Computational and Mathematical Engineering at Stanford University and a Knight-Hennessy Scholar. I’ve done much of my research at the Stanford Computational Policy Lab, where I worked as a data scientist for two years prior to starting my Ph.D. Before that, I graduated from Harvard University with bachelor’s degree in pure mathematics, where I studied set theory and ergodic theory, and from the University of Oxford, where I got a master’s degree in the history of science. After graduating college, I also worked as a legal intern for the ACLU of Wisconsin.

These days, I like to spend a lot of my time cooking and learning new languages, playing the guitar less well than I used to (which is, like, really saying something), and getting outside.