Lijun’s research lies at the intersection of optimization, statistics, and machine learning, where I work on solving large-scale and high dimensional optimization problems. By exploring ideas and techniques such as Frank-Wolfe, strict complementarity, and the leave-one-out argument in these fields, I have been able to design computationally and statistically efficient algorithms for both classical convex optimization problems such as semidefinite programming, and newly arising nonconvex problems.