Masashi Sugiyama

Masashi Sugiyama is a Professor at The University of Tokyo, Department of Complexity Science and Engineering, Graduate School of Frontier Sciences. He is also an Affiliated Professor at the Department of Computer Science, Graduate School of Information Science and Technology, and the Department of Information Science, Faculty of Science, at The University of Tokyo. His research interests include statistical machine learning, data mining, signal and image processing, robot control, and related areas. Sugiyama is also a Director at the RIKEN Center for Advanced Intelligence Project. He has authored several books on topics such as weakly supervised learning, statistical machine learning, reinforcement learning, density ratio estimation, and covariate shift adaptation.

Masashi Sugiyama is a Professor at The University of Tokyo and Director of the RIKEN Center for Advanced Intelligence Project. His research focuses on Machine Learning, Data Mining, and Artificial Intelligence, with an emphasis on adapting models to non-stationary environments. He has contributed significantly to covariate shift adaptation, importance estimation, and robust learning under label noise. His work explores techniques such as direct importance estimation, density ratio estimation, and local Fisher discriminant analysis for supervised dimensionality reduction. Sugiyama's research also addresses challenges in deep learning, including adversarial learning and change-point detection in time-series data.