I have broad research interests in optimization and theoretical foundations of machine learning. One of the core themes of my research is leveraging structure in optimization. Some concrete examples include:

• Linear subspace structure for adaptive optimization algorithms

• Hypergraph structure for capturing higher-order interactions in datasets

• Graph structure for learning-based search algorithms

I recieved by PhD from the University of Chicago, where I was very fortunate to be advised by Professors Lorenzo Orecchia and Rebecca Willett.