I work on statistical genetics. I am currently a postdoctoral fellow at the Broad Institute, Harvard T.H. Chan School of Public Health, and Harvard Medical School, mentored by Luke O'Connor and Alkes Price. I received my PhD in Bioinformatics from UCLA, advised by Bogdan Pasaniuc. Before that, I studied Computer Science at Zhejiang University.
My research focuses on understanding how genetic variation shapes human phenotypes and diseases and applying these insights to identify causal genes, biological mechanisms, and therapeutic opportunities. Rapidly expanding population-scale datasets that link genetic variation to rich phenotypic information make it possible to study naturally occurring perturbations of human biology at scale. I develop computational and statistical methods to extract reliable insights from these complex genetic, proteomic, and functional genomic data.
Until recently, our ability to jointly profile protein abundance and common and rare genetic variation at population scale was limited. My work uses these emerging data to connect genetics to proteins to disease by studying how local and distal genetic variation shapes protein abundance and how coding mutations alter protein function, to prioritize causal genes and disease mechanisms.
Polygenic scores combine variants across a person's genome, weighting each by its estimated effect, and have the potential to improve estimates of lifetime disease risk. Using these scores in clinical decisions requires knowing how much confidence to place in each individual's prediction. My work develops uncertainty metrics that quantify this reliability across ancestry backgrounds and socio-environmental contexts.
Biobanks link genetic and health data at population scale, enabling us to study the genetic architecture of complex traits. Their scale and diversity require statistical methods that remain accurate across study sizes, ancestry backgrounds, and genetic architectures. I develop methods to measure how much common genetic variation contributes to traits, test whether genetic effects are shared across ancestry backgrounds, and map heritability across genes and allele frequencies to prioritize disease-relevant genes.