I am a PhD student at MIT’s Operations Research Center.
I work with Colin Fogarty on problems in causal inference. My earlier work centered around observational studies and the associated sensitivity analyses. More recently I have been working on inference procedures in settings without the risk of unmeasured confounding.
Before coming to MIT, I graduated from Bowdoin College in 2017 with a B.A. in Mathematics. Before starting operations research, I did some research in biology, quantum chemistry, analytic number theory, and random matrix theory.
PhD Student in Operations Research, 2022
Massachusetts Institute of Technology
B.A. in Mathematics, 2017
Bowdoin College
Teaching assistant for an undergraduate course which aims to provide students with a theoretical understanding of fundamental techniques in data science, including linear regression and hypothesis testing, as well as a toolkit for practical implementation of statistical techniques.
Duties: Assisting students, leading recitations, holding office hours, grading midterm and final exams.
Hypothesizing elaborate cause-effect relationships is a dangerous game. On one hand, if data supports an elaborate relationship, then the underlying model is well supported. However, elaborate relationships often invovle testing several different outcomes. For instance, to claim that an economic intervention is effective, examining its impact through several metrics helps increase credibility. When testing multiple outcomes, corretions for multiple comparisons are necessary to avoid making errors at a high rate; these corrections often dramatically reduce the power of statistical tests. Working with Colin Fogarty and Matt Olson, we have approached the problem of testing several one-sided hypotheses simultaneously with high power in the context of observational studies. We are in the process of publishing the results, and a pre-print is currently available on arXiv. Code to implement the methods in the paper is available here.