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 passionate about data science education, and I am particularly interested in exploring research-based methods, and evaluating their effectiveness in teaching data science. I have served as a technical mentor at Stanford Data Science for Social Good summer program, where I led a team of 3–4 fellows to tackle a data science project with positive social impact over eight weeks. I have written a guide for data science for good mentors.

In my free time, I enjoy reading, hiking and baking.

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