I am an assistant professor at Harvard University in the statistics department. My research focuses on problems in probability and statistics that are motivated by machine learning. I graduated with a PhD in statistics from Columbia University in 2019 where I worked in collaboration with Peter Orbanz and Arian Maleki on limit theorems for dependent and structured data. For two great years (2019-2021), I was a postdoctoral researcher at Microsoft Research New England.
Broadly, I am interested in developing probability tools for modern machine learning and in establishing the properties of learning algorithms in structured and dependent data contexts. Notably, my research extended limit theorems for dependent data and matrices, studied the properties of embedding methods and established the properties of resampling methods such as the cross-validation and bootstrap method. My current work is motivated by generalization and concentration bounds, stable matching problems and random matrix theory.