I am a doctoral student in biostatistics at the Harvard T.H. Chan School of Public Health, supported by an NSF Graduate Research Fellowship. My advisor is Rui Duan.
My research develops theory and methods for data integration problems in high dimensions. I’m motivated by challenges posed by electronic health records and genotype data, especially pertaining to model shifts across environments and embeddings for heterogeneous temporal data. I’m also interested in minimax testing problems and the philosophy of science.
Before coming to Harvard, I was a mathematics major at the University of Florida, where I explored interests in neuroscience, bioinformatics, and functional programming. I spent a summer working with Tim Randolph at the Fred Hutch in Seattle, WA, and served on the executive board of UF’s Center for Undergraduate Research.
You can find me on GitHub and Google Scholar.
(Oct. 2024) I will serve as a reviewer for ICLR 2025.
(Aug. 2024) I gave a talk at JSM 2024 titled Latent factor point processes for patient classification with electronic health records.
(May 2024) I am honored to receive the Rose Traveling Fellowship from the Harvard School of Public Health. I will use the funds to visit my colleague Julien Chhor at the Toulouse School of Economics.
(April 2024) I gave a 60-minute talk at the Harvard Biostatistics student seminar titled ‘Building linear models from reference data: recent results and open problems’. Thanks to my friend and colleague Emma Crenshaw for the invitation.
(Feb. 2024) I will serve as a reviewer for ICML 2024.
(Sep. 2023) Our paper Multi-task learning with summary statistics was accepted to NeurIPS 2023.
(Aug. 2023) I gave a contributed talk at JSM 2023 titled Multi-task learning with summary statistics.
(July 2023) We have a new paper on arXiv: Multi-task learning with summary statistics.
(Feb. 2023) Our paper Generalized matrix decomposition regression: estimation and inference for two-way structured data was accepted by the Annals of Applied Statistics.