My broad interests lie in understanding the limits of learning by trying to answer questions such as when is learning possible and when is it not under certain model assumptions? Such questions are particularly interesting when posed in constrained settings, for instance, constraints on computational complexity or adversarial robustness. Understanding such fundamental limits foster the design of provably efficient and reliable Machine learning methods.

More recently, I have been fascinated with statistical and causal properties of interpolating estimators in overparameterized model classes. You can find our recent work on this topic here. For a general overview of my work, please see my publications.

Brief Bio: