§ Research Overview

My research focuses on the design and analysis of novel data-driven gradient-based methods to support decision making under uncertainty. Some of my recent interests include:

1. Dynamic optimization: algorithms for Markov decision process (MDP) & reinforcement learning (RL).

2. Robust dynamic optimization: formulation & algorithms for MDPs/RL with uncertain transition kernel/cost.

3. Minimax optimization: optimal gradient-based methods for structured minimax problems.

4. Optimization for machine learning: understanding and improving gradient-based methods in practice.

You can also find some projects I have done in collaboration with industry here on treatment planning, smart transportation, and recommendation systems. I am constantly interested in applications in broader areas.

§ Journal

§ Conference

§ Preprints/Working Papers

§ Research Awards

Part of my research has been kindly acknowledged by