معرفی
Kengo Kato is a Professor in the Department of Statistics and Data Science at Cornell University, affiliated with the College of Arts and Sciences. Previously, he held a position at the Faculty of Economics at The University of Tokyo and served as a visiting scholar at MIT’s Department of Economics. His research focuses on Mathematical Statistics, Applied Probability, and Econometrics, with an emphasis on high-dimensional statistical models and optimal transport theory.
His work spans topics such as statistical inference in optimal transport, Gromov-Wasserstein distances, bootstrap methods, and high-dimensional data analysis. Notably, he is Editor-in-Chief of the journal Bernoulli (2025-2027) and serves as an associate editor for Japanese Economic Review and Journal of Statistical Planning and Inference. His research bridges theoretical advancements with practical applications in econometrics and machine learning.
Recent publications emphasize foundational results in optimal transport theory, including limit laws for Gromov-Wasserstein alignment, stability of entropic maps, and statistical guarantees for sliced Wasserstein distances. His work on bootstrap techniques addresses challenges in high-dimensional and spatial data analysis, offering robust inference methods for modern datasets.
While no formal advisees are listed, his contributions to statistical theory and methodology suggest significant mentorship in graduate training programs. His research is supported by Cornell’s interdisciplinary environment and collaborations within affiliated institutes.
