spacer


Masashi Sugiyama / Professor / Division of Transdisciplinary Sciences
Department of Complexity Science and Engineering / / Machine learning and statistical data analysis
http://www.ms.k.u-tokyo.ac.jp/sugi/index.html

Career Summary
1997/3 Graduated from Department of Computer Science (Tokyo Institute of Technology)
2001/3 Received Ph.D. in Engineering (Tokyo Institute of Technology)
2001/4-2002/12 Assistant Profesor (Tokyo Institute of Technology)
2003/1-2014/9 Associate Profesor (Tokyo Institute of Technology)
2014/10-present Professor (University of Tokyo)
Educational Activities
Graduate school: Advanced Data Analysis
Undergraduate school: Statistical Machine Learning, Intelligent Systems, Statistics and Optimization
Research Activities
We are investigating theory and application of machine learning and statistical data analysis. Recently, we are particularly interested in weakly supervised learning and noise-robust learning.
Literature
Sugiyama, M. and Kawanabe, M. Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation, MIT Press, 2012.
Sugiyama, M., Suzuki, T., and Kanamori, T. Density Ratio Estimation in Machine Learning, Cambridge University Press, 2012.
Sugiyama, M. Statistical Reinforcement Learning: Modern Machine Learning Approaches, Chapman and Hall/CRC, 2015.
Sugiyama, M. Introduction to Statistical Machine Learning, Morgan Kaufmann, 2015.
Other Activities
2016/7-present Director, RIKEN Center for Advanced Intelligence Project (cross-appointment)
spacer
Future Plan
We aim to develop mathematically-grounded, practical, and versatile machine learning technologies that enable non-experts to analyze data proficiently.
Messages to Students
Machine learning and statistical data analysis are technologies that have a wide range of applications in science, engineering, and business, and they are making rapid progress recently. I hope students with diverse backgrounds such as mathematics, natural science, engineering, and economics will join us in this exciting research field.
top