Yiqun Chen

I am a Stanford Data Science Postdoctoral Fellow working with Professor James Zou. My research focuses on quantifying, calibrating, and communicating the uncertainty in modern data analysis, with applications to biomedical and health data. I am also interested in exploring how to make machine learning methods more reliable and equitable. In addition to my methodological interests, I have enjoyed working on collaborative projects with scholars in public health, human-computer interactions, software engineering, and biology.

I received my Ph.D. in Biostatistics from the University of Washington, where I was advised by Professor Daniela Witten. During my graduate training, I am the recipient of a young investigator award at CROI 2020, the best paper award at WNAR 2021, a student research award at NESS 2022, a University of Washington Outstanding Teaching Assistant Award, and the Thomas R. Fleming Excellence in Biostatistics Award. I completed my undergraduate degrees in Statistics, Computer Science, and Chemical Biology at the University of California Berkeley.