I am a machine learning researcher working on sequential decision-making in feedback loops, i.e., the multi-armed bandit problem. With backgrounds on online optimization and interdisciplinary collaborations, I develop algorithms with theoretical guarantees that have important real-world

Modern practitioners use machine learning to guide their decisions in applications ranging from marketing and targeting to policy making and clinical trials. To embrace automated decision-making in closed loops, it is crucial to make decisions safely and efficiently, as poor decisions waste human resources and result in social and economic costs. To this end, I propose to build a comprehensive research program on closed-loop decision problems with an emphasis on humans in the loop. Specifically, I will investigate novel ways of interacting with humans in realistic settings, develop mathematical algorithms and theory for complex models, and apply them to salient problems in biology, psychology, and economics.