The focus of Laber Labs is the development of practical yet mathematically rigorous methodology for data-driven decision making.

Major research areas are causal inference, non-regular asymptotics, optimization, and reinforcement learning. Primary application areas include precision medicine, artificial intelligence, adaptive conservation, and the management of infectious diseases. We also have a small robotics laboratory that we use to study cooperative and competitive decision problems.

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See here for a simple explanation of our work.