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.
- Meeting Form Goals
- ASA Data-expo CDC public-use data IMPACT website ISI STATBlog NC State Statistics Dept.
- Bandit Workshop Playlist Shiny App – Simulate infection disease LaserCats AI with Bayesian Q Learning Invent with pygame Another intro to pygame OSIM2
- Short talk on IQ-learning Inference after model selection Test error for classification Set-valued dynamic treatment regimes
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