Decision Optimization Lab.

We focus on system design from a decision-making perspective, defining how decisions are structured and coordinated within complex systems to achieve their objectives. Our research targets systems whose behavior emerges from interactions among components, including logistics, transportation, and healthcare systems.

In game theory, a game is a formal model of a strategic situation where multiple decision-makers interact, each choosing actions that influence not only their own outcome but also the outcomes of others. We are interested in using games to represent and analyze interactions among multiple agents within a system.

Reinforcement Learning (RL) is a type of machine learning where an agent learns to make optimal decisions by interacting with an environment and receiving rewards or penalties for its actions. We focus on applying reinforcement learning to sequential decision-making problems in systems such as operations.