I am a final year PhD student in EECS at MIT. I am currently applying for faculty positions in Statistics and Computer Science (as of Fall 2023). I am extremely fortunate to be advised by Caroline Uhler and David Sontag.

Ny research focuses on building the statistical and computational foundations for AI-driven decision-making in scientific applications, and I work on methods for causal structure learning, experimental design, and causal representation learning.

As a member of the Eric and Wendy Schmidt Center, my work is grounded by problems in cellular biology, e.g. predicting the effect of a drug in novel cancer cell lines.

Teaching and Organizing. I really enjoy teaching, mentoring, and organizing academic initiatives. Check out the lecture notes and recordings for the causality class that I developed for MIT’s 2023 January term, and join the ongoing talk series, Causality, Abstraction, Reasoning, and Extrapolation (CARE), that I am co-organizing.

Communication. I believe that communicating technical material is one of the most important and most challenging activities in a research career. As a member of the EECS Communication Lab, I coach researchers on how to communicate more effectively.

Software. I like writing good, easy-to-use code (when time permits 😅), and I am the main developer of causaldag, a Python package for creating, manipulating, and learning causal graphical models. Shoot me an email to chat about the package or my work!

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