Daisuke KURISU

Daisuke KURISU is an Associate Professor at The University of Tokyo, affiliated with the Graduate School of Economics, Department of ECON. His research focuses on Mathematical Statistics, Econometrics, and Statistical Machine Learning, with particular interest in time series, spatial data, random object analysis, and high-dimensional data analysis. He has published extensively in leading journals such as the Journal of the Royal Statistical Society, Bernoulli, and the Journal of Econometrics. KURISU has also received several awards, including the JSS Ogawa Award and the University of Tokyo Excellent Young Researcher Award.

Daisuke Kurisu is a researcher in Mathematical Statistics, Econometrics, Machine Learning, and Applied Probability. His work focuses on developing statistical methods for nonlinear time series models, high-dimensional spatial data, and functional data analysis. Kurisu's research includes adaptive deep learning, Gaussian approximation, and nonparametric regression techniques for locally stationary processes. He also explores inference under measurement error and causal inference in geodesic spaces. His publications address challenges in financial data, environmental impact analysis, and econometric modeling with complex data structures. His research combines theoretical rigor with practical applications in economics and statistics.