Jisu Kim’s research lies at the intersection of statistics, geometry, and topology, with particular emphasis on Topological Data Analysis (TDA). He develops statistical methodologies for analyzing geometric and topological structures in data, particularly through persistent homology. His work bridges the gap between statistical and topological frameworks by designing statistical tools tailored to topological contexts and vice versa. He is engaged in the theoretical foundations of statistical inference on manifolds, including estimation of intrinsic dimensionality and reach. He also explores applications of TDA to machine learning. He also contributes to the open source software, notably co-authoring the R package TDA for statistical topological data analysis.
Email: jkim82133 [AT] snu [DOT] ac [DOT] kr
Office: 25-335
Mailing address:
KIM, Jisu
1 Gwanak-ro, Gwanak-gu
Department of Statistics, Seoul National University
Seoul 08826, Republic of Korea
GPS: +37.4587667°, +126.9499825°
Department of Statistics, Seoul National University, Seoul, Republic of Korea
DataShape, Inria Saclay, France
Non Permanent Researcher [Mar 2020 - Mar 2023]
PostDoc [Feb 2020 - Nov 2018]
Advisor: Frédéric Chazal
Laboratoire de Mathématiques d’Orsay, Université Paris-Saclay, France
Carnegie Mellon University, Pittsburgh, United States of America
Ph.D., Statistics & Machine Learning (joint program) [Aug 2013 - Dec 2018]
Advisor: Larry Wasserman and Alessandro Rinaldo
M.S., Statistics [Aug 2013 - May 2014]
Seoul National University, Seoul, Republic of Korea
B.S., Mathematics, Computer Science(double major), Statistics(double major) [Mar 2006 - Aug 2013]
Graduated Summa Cum Laude ( first honors in College of Natural Science )