I joined Carnegie Mellon’s Statistics and Data Science Department as an assistant teaching professor in 2019. Before that, I received my PhD in Statistics at Harvard University, where I was advised by Tirthankar Dasgupta and Luke Miratrix. As a graduate student, I received an NSF Graduate Research Fellowship to study the design and analysis of complex experiments and observational studies. At this point my bio becomes somewhat repetitive: I did my undergraduate at Carnegie Mellon from 2010 to 2014, where I received a B.S. in Economics and Statistics and a B.A. in Professional Writing.
Most of my work is an interplay of causal inference and experimental design. Broadly, I work on the design and analysis of (1) complex experiments like factorial designs, (2) quasi-experiments like regression discontinuity designs, and (3) observational studies, where methods like matching can be used to approximate randomized experiments. I also work on applications in economics, education, engineering, medicine, public policy, and text analysis.