Research
Our interests lie in the intersection of reinforcement learning, mathematical optimization and data analytics. Our research is centered around theoretical and practical aspects of decision-making problems in uncertain and dynamic environments. We utilize tools from control theory, machine learning, and information theory.
Our current active research topics include:
- Machine learning: reinforcement learning, data-driven decision making, distributional shift adaptation
- Stochastic programming: distributionally robust optimization
- Control theory: stochastic control, approximate dynamic programming