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I am a Ph.D. student in computer science at Cornell University, where I started in 2023. I am fortunate to be co-advised by Prof. Giulia Guidi and Prof. Anil Damle. I have also worked with Prof. Chris De Sa, who is on my committee. Before Cornell, I earned an M.S. in statistics from the University of Chicago and a B.S. in statistics from East China Normal University.
I am interested in making computers do useful things efficiently, from LLM inference to biobank-scale genomic analyses. I exploit problem structure to co-design high-level algorithms and hardware-aware implementations. My current focus is on using high-performance computing to accelerate research in the life sciences, particularly studies of gene-by-environment interactions.
I enjoy finding analogies between seemingly unrelated fields: sometimes, the tool you need already exists elsewhere. In my work, this has meant drawing on Gaussian source coding to quantize LLM weights, and expressing genotype matrix-vector products through graph representations as sparse triangular solves.
I learned to program believing that it’s extraordinarily important that we in computer science keep fun in computing. I wanted to become a software engineer so that playing with the spirit that lives in the computer could be both my job and my hobby. The rise of LLMs has made me rethink that dream: I still value the craftsmanship of programs written for people to read, and feel uneasy when code is treated as a mass-produced commodity. At the same time, LLMs make it easier to try out new ideas, which makes research even more rewarding, so I am now leaning toward a research scientist role after my Ph.D. :)
I will present a poster at SC26 in Chicago in November 2026. Come say hello!
My favourite foods are fried chicken, pho, and oysters.