Biography

There’s a special thrill when a difficult concept I have been trying to understand finally clicks. I chased this feeling as an undergraduate studying physics at the Massachusetts Institute of Technology and fell in love with the mathematical rigor physics uses to explain fascinating phenomena. After starting a PhD in astronomy at Northwestern University, a research internship at the Center for Computational Astrophysics in the Flatiron Institute encouraged me to pivot towards machine learning research, leading me to leave the program with my Master's. During this internship, I worked on machine learning for astronomy and realized how impossible it is to understand the insights these models gain when treated as black boxes. This revelation sparked my passion for interpretability—breaking down what information successful machine learning models have actually learned. As a PhD student at the University of Cambridge, my research will focus on developing tools to peer inside these black boxes in physics and astronomy, with hopes to extend these methods to other fields. I am incredibly honored to join the Gates community and will work hard towards ensuring AI serves as an insightful partner in advancing scientific discovery.

Previous Education

Massachusetts Institute of Technology Physics
Northwestern University Astronomy