About

I’m a second year Ph.D. student in the Department of Statistics at Harvard University. Prior to coming to Harvard, I studied probability as an undergrad at McGill University — in my hometown of Montréal — where I worked under the supervision of Prof. Vojkan Jakšić and Dr. Renaud Raquépas in the field of entropic information theory. At Harvard, I’m grateful to be advised by Prof. Yue M. Lu in applied mathematics where my research focuses on the mathematical foundations of AI. In my work, I employ tools from classical and high-dimensional probability, and random matrix theory, to provide a rigorous understanding of phenomena in machine learning.

Aside from my principal work mentioned above, I’ve recently had the pleasure of working with Prof. Tim Hoheisel, Prof. Michael Friedlander, and Prof. James V. Burke on developing robust algorithms in convex optimization.

Current Research interests

• High-dimensional problems in probability and statistics
• Theoretical machine learning
• Mathematical optimization