I received my PhD in Computer Science from Harvard University where I was advised by Yaron Singer.
My research is at the intersection of machine learning and algorithms. My most recent line of work on adaptivity develops parallel algorithms for machine learning applications. For a broad class of optimization problems, these new algorithms obtain exponential speedups in parallel runtime. I am also interested in the closely related area of optimization from samples, where sampled data and machine learning are used for decision tasks, instead of prediction tasks. For more details, my publications can be found here.
As a graduate student, I received a Google PhD fellowship and a Smith family graduate science and engineering fellowship. Before my PhD, I was an undergraduate student at Carnegie Mellon University.