We study the statistical foundations of algorithmic decision making.
Our research explores how data can be used to make better decisions in complex and uncertain environments. We are particularly interested in optimal policy learning in dynamic economic systems and in data-driven decision making under uncertainty.
We use tools from reinforcement learning, optimal control, stochastic programming, and optimization. We aim to contribute both to theoretical understanding and to practical algorithmic solutions that are robust and scalable.
Director
