Regular biography
Ilmun Kim is an Associate Professor in the Department of Mathematical Sciences at Korea Advanced Institute of Science and Technology (KAIST). He is also affiliated with the Graduate School of AI for Math (AI4Math). Kim previously served as an Assistant Professor in the Department of Statistics and Data Science and Department of Applied Statistics at Yonsei University. He held a Research Associate position in the Statistical Laboratory at the University of Cambridge, working under Professors Richard Samworth and Rajen Shah. Kim received his Ph.D. in Statistics and Data Science from Carnegie Mellon University, advised by Professors Larry Wasserman and Sivaraman Balakrishnan. His research interests include nonparametric inference, distribution-free inference, minimax testing, semi-supervised inference, differential privacy, and kernel-based methods.
Scholar-generated biography
Ilmun Kim is a statistician whose research focuses on developing robust and efficient nonparametric statistical methods for hypothesis testing. His work includes two-sample tests, conditional independence, and multivariate distribution comparisons, with an emphasis on permutation-based and kernel methods. Kim has contributed to the development of minimax optimal tests, permutation-free approaches, and techniques for high-dimensional data analysis. His research also extends to differential privacy in statistical inference and goodness-of-fit tests for complex models. Kim's publications highlight the use of U-statistics, projection averaging, and aggregated tests to improve statistical power and validity in challenging data settings.