Learning Efficient Interpretable Policies on Experimental Data
Han Wu, Sarah Tan, Weiwei Li, Mia Garrard, Hanson Wang, Daniel Jiang, Adam Obeng, Eytan Bakshy
I am a Research Scientist on the Adaptive Experimentation team. Prior to Meta, I completed my PhD in Statistics at Cornell University, and was also based at the University of California, San Francisco where I worked on interpretability techniques for healthcare applications. My current research focuses on causal inference and reinforcement learning, with a particular focus on developing safe and robust experimentation techniques. I am also interested in interpretability and algorithmic fairness.
Interests
Causal inference, interpretability, reinforcement learning, algorithmic fairness