In September 2024, I am starting as a Postdoctoral Research Associate at CMU, supervised by Pradeep Ravikumar. Previously, I was a PhD Student at MIT, where I was advised by Caroline Uhler and David Sontag.

I think of myself as working on the foundations of Pragmatic Data Science, a framework for statistical analysis that emphasizes downstream decision-making. My research touches on several topics in causality (causal effect estimation, causal structure learning, and causal representation learning) and combines tools from high-dimensional statistics, combinatorial optimization, and machine learning.

On the application side, I am broadly interested in AI4Science and the unique challenges encountered when bringing AI into scientific domains. Right now, I am focused on applications of my work in drug discovery and cellular biology.

(Last update: August 26th, 2024)

Teaching and Service. I developed and taught a seven-lecture course on causality for MIT’s 2023 January term, check out the lecture notes and recordings.

I co-organize the Causality, Abstraction, Reasoning, and Extrapolation (CARE) talk series, and I am serving as Publication Chair for CLeaR 2025.

During my PhD, I was a member of the EECS Communication Lab, where I coached researchers on technical communication tasks such as paper writing, poster design, and oral presentation.

Software. During my PhD, I developed and maintained causaldag, a Python package for creating, manipulating, and learning causal graphical models.

Other materials. I try to keep an up-to-date repository of slides from my talks and posters from conferences and workshops.