Research
Mathematical optimization theory and numerical algorithms for large-scale problems in science, engineering, economics, and AI.
Research profile
Our work focuses on mathematical optimization, with applications in inverse problems, game theory, power systems, optimal control, and machine learning. We develop mathematical foundations as well as numerical algorithms for large-scale optimization problems.
Selected directions
Optimization and variational inequalities
First-order methods, splitting schemes, stochastic approximation, variance reduction, and complexity guarantees for structured and hierarchical problems.
Games and learning
Equilibrium computations, generalized Nash games, multi-agent learning, and dynamics in uncertain and time-varying environments.
Optimization for AI and inverse problems
Derivative-free and bilevel methods, learning-informed optimization, and scalable computational techniques for data-driven applications.