aboutI'm a final-year Ph.D. student in computer science at Harvard in the Statistical Reinforcement Learning Lab. My research interests lie at the intersection of adaptive experimentation, reinforcement learning, and statistical inference. I am advised by Susan Murphy and Lucas Janson. I am supported by an NSF Graduate Research Fellowship and the Siebel Foundation (2023 Siebel Scholar).
Much of my research has focused on developing methods for statistical inference for data collected with bandit and reinforcement learning algorithms, i.e., adaptively collected data. Since fall 2020, I have also been working to design the reinforcement learning algorithm and statistical inference approach to be used for the primary analysis for Oralytics, a mobile health micro-randomized trial for oral health coaching in collaboration with Oral-B and researchers at UCLA and UMichigan.
I previously worked on natural language processing and deep learning with Sasha Rush, Sam Bowman, and Yann LeCun. I also previously interned at Apple's HealthAI team in Seattle, Facebook AI Research in New York, and at eBay New York on the homepage recommendations team.
- September 2022: I am a 2023 Siebel Scholar!
- Summer 2022: I am interning at the Apple HealthAI team in Seattle working on statistical inference methods for large-scale mobile health applications (paper forthcoming).
- June 2022: I am very excited to be organizing an invited session at the 2022 Institute of Mathematical Statistics Annual Meeting on "Inference Methods for Adaptively Collected Data". The speakers will be Nathan Kallus, Koulik Khamaru, Evan Munro, and myself! Joseph Jay Williams and Nina Deliu will chair the session.