I am currently an assistant professor in the Department of Mathematical Sciences at the Korea Advanced Institute of Science and Technology (KAIST). Previously, I was a ML scientist at AWS AI Labs. Before that, I was a Neyman Visiting Assistant Professor in the Department of Statistics at UC Berkeley, where I was very fortunate to be supervised by Bin Yu. Prior to that, I was a Postdoctoral fellow at UC Berkeley’s Foundations of Data Analysis (FODA) Institute and Berkeley Institute for Data Science (BIDS). I obtained my PhD in Statistics at the University of Chicago where I was very fortunate to be advised by Rina Foygel Barber.
My research is centered on high-dimensional statistics and machine learning, with a focus on sparse and low rank optimization, local graph clustering, interpretable machine learning, and statistical learning under distribution shifts. I am also interested in the applications of statistical and optimization methods to diverse scientific areas such as medical imaging, population genetics, and cosmology.
Education
The University of Chicago
Seoul National University
- M.S., Statistics, 2013 (Advisor: Byeong U. Park)
- B.S., Statistics, B.A., Economics, Minor in Mathematics, 2011
Email: haywse@kaist.ac.kr
▸ For Prospective Students: If you are looking for a thesis topic or research opportunity, check out my Google Scholar or GitHub repo to see if any of the topics might be a good fit. My current interests include statistical learning under distribution shifts (such as domain adaptation and transfer learning), graph neural networks, and applications with generative models.