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