Research

Research Directions

My research interests are optimization problems arising in machine learning, operations research and other applications. I am working on the following topics:

• Computational complexities for gradient-based, gradient-free (zeroth-order) and primal-dual algorithms for solving nonconvex optimizations (optimization on Riemannian manifolds in particular) and their large-scale applications.
• Computational complexities for deterministic and stochastic hierarchical (minimax and bilevel) problems with applications in operations research and machine learning.
• Distributed optimization algorithms, including decentralized and federated learning.
• Theories for reinforcement learning, and their applications in large language model (LLM) alignment and reasoning.

I am also interested in theory and computation for supervised and unsupervised learning in general.