Kelly W. Zhang
kellywzhang@seas.harvard.edu
Office 2.340, Harvard SEAS SEC
[CV]    [Google Scholar]

about

I'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). Post-graduation plans: In September 2024 I will start as a Lecturer (Assistant Professor) at Imperial College London in the Mathematics Department (statistics section). I will also be a faculty member in the Imperial-X, an initiative driving innovation in machine learning, artificial intelligence and data science. I will spend a postdoc year (fall 2023-summer 2024) at Columbia Business School working with Daniel Russo and Hongseok Namkoong.

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.