My research focuses on the intersection between optimization and machine learning. In particular, I try to establish the theoretical guarantees of machine learning algorithms, including their convergence properties and generalization behaviors. For example, I previously worked on the convergence analyses of variance reduction methods for structured nonconvex stochastic optimization that are common in many machine learning applications such as GANs, meta-learning, and reinforcement learning. Currently, I am working on differentially private stochastic optimization due to the increasing demand for data privacy in the industry. In addition to that, I am also interested in understanding the generalization of nonconvex optimization problems.