I am an Assistant Professor in Statistics at the University of Massachusetts Amherst. Prior to joining UMass, I was a postdoctoral scholar at Stanford Biomedical Data Science advised by Professor Chiara Sabatti, studying genetic underpinnings of severe mental disorders. My research aims to pinpoint important genetic variants, identify similar and different factors underlying several mental disorders, and construct polygenic risk scores that applies to a diverse population.

I completed my PhD in Statistics at Stanford University in 2021, advised by Professor Emmanuel Candès. My dissertation studied how to infer model coefficients in a high-dimensional generalized linear model. High-dimension refers to the situation when the number of variables is large, or even comparable to the number of observations. Standard statistical methods often exhibit surprising behavior in this setting, and my research develops statistical theory and methods to achieve valid inference in the high-dimensional setting.

I am broadly interested in applying statistics and data science for positive social impacts. Currently, I am developing data-driven methods to estimate and address nutrition insecurity. You can find a recent presentation here.

I am passionate about teaching and data science education, and I am particularly interested in exploring research-based methods, and evaluating their effectiveness in teaching data science.

Download my CV .