My main research is on numerical optimization: theory, algorithms, and applications. I am currently focusing on efficient methods (both deterministic and stochastic) for convex optimization and nonconvex optimization, with applications in signal/image processing, statistics, engineering, machine learning, and data science. I am also working on proximal interior-point methods for convex constrained programming with applications in conic programming and SDP relaxations.