Bio
Byol Kim is an Assistant Professor in the Department of Statistics at Sookmyung Women’s University, which she joined in September 2024. Before that she was a postdoctoral scholar in the Department of Biostatistics at the University of Washington and a UW Data Science Postdoctoral Fellow at the eScience Institute, working with Ali Shojaie .
She earned her PhD in Statistics from the University of Chicago in 2021, advised by Rina Foygel Barber and Mladen Kolar , with a thesis titled Uncertainty Quantification under Weak Assumptions. She also holds an MS in Statistics from the University of Chicago, advised by Lek-Heng Lim , and a BA in Applied Mathematics from Yale, awarded cum laude.
Her research develops ways to quantify uncertainty while leaning on as few assumptions as possible. One line of work looks for scientifically meaningful structure in complex, high-dimensional data — such as the differential networks that capture how relationships among many variables shift from one condition to another. Another studies the black-box models now common in prediction: building predictive confidence sets with distribution-free guarantees, and asking what can, and cannot, be learned about a learning algorithm’s behavior from data alone. Much of this work has grown out of collaborations with scientists in medical imaging and neuroscience.
Education
- PhD, Statistics — The University of Chicago, 2021.
- MS, Statistics — The University of Chicago, 2015.
- BA, Applied Mathematics — Yale University, 2009 (cum laude).
Appointments
- Assistant Professor, Department of Statistics, Sookmyung Women’s University (Sep 2024–present).
- UW Data Science Postdoctoral Fellow, eScience Institute, University of Washington (Apr 2022–Aug 2024).
- Postdoctoral Scholar, Department of Biostatistics, University of Washington (Sep 2021–Aug 2024).