I’m a Blavatnik fellow at Harvard University studying the intersection of statistical machine learning and digital phenotyping. My research aims to explore machine learning methods that offer a safe, reliable, and intepretable mechanism for quantifying and improving human quality of life in major clinical applications such as mental health, rehabilitation, and chronic disease management. I’m particularly interested in domains of study operationalized by the ubiquity of continuous data collection via personal digital devices (e.g., smartphones and wearables). These topics include reinforcement learning, state space modeling, and missing data.
I earned a Ph.D. in biostatistics from Harvard under the co-supervision of Dr. Jukka-Pekka Onnela and Dr. Junwei Lu. My Ph.D. was supported by the Harvard Prize Fellowship, the T32 Psychiatric Epidemiology and Biostatistics Grant, and the T32 Cancer Training Grant. I also hold an A.M. in biostatistics from Harvard University and a B.A. in mathematical biology from the University of Pennsylvania.
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