My research is generally in machine learning and optimization. The primary focus of my PhD research has been the theory of optimization, with a particular emphasis on precisely understanding the oracle complexity of convex, non-convex, and distributed optimization problems. In addition to my work in optimization, I have also been interested in efforts to understand modern, highly overparametrized machine learning models through the lens of implicit regularization. Earlier in my PhD, I also enjoyed working on fairness in ML and on adaptive data analysis