Professor of Statistics
Seoul National University
Interested in statistical theory and methods for Non-Euclidean, High-Dimensional data analysis, and Data Privacy
I am a Professor of Statistics at the Seoul National University. Before I joined Seoul National University, I spent seven years at the University of Pittsburgh, after completing my PhD at the University of North Carolina at Chapel Hill.
Research interest lies in the theoretical study and applications of modern Statistics and Data Science in the analysis of data that lie on non-standard spaces. This context includes the high-dimension, low-sample-size (HDLSS) situation, non-Euclidean data analysis, the interplay between geometry and statistics, and data fusion. In particular, models and methodologies for dimension reduction, visualization of important variation and hypothesis testings need to be developed with special care for these modern data situations. Particular applications include analysis of directions, landmark-based and skeletally-modeled object shapes, data in stratified spaces or from multiple sources, and retrieving low-dimensional geometric structures in high-dimensional data. I am also interested in statistical issues in Data Privacy, including Differential Privacy and Synthetic Data Generation.
I have authored two books and coauthored one more, all in Korean:
2025-03-20. The third edition of the classical statistics textbook “일반통계학 (Foundations of Statistics)” is now on sale. I coauthored this book with my colleagues from the department.
2025-03-07. I have received Education Award from the College of Natural Science, Seoul National University. Thanks!
2025-03-04. After a long wait, my research article on DP multivariate statistics, with Minwoo, Jongheoyk and Seungwoo, is now accepted for publication in EJS. This work has revealed several interesting facts on the differential privacy, including that the multivariate Laplace additive mechanism has higher utility under low privacy levels of GDP.
Last updated: April 02, 2025