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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

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:
Undergraduate school:
Research Activities
We investigate the theory and application of machine learning and statistical data analysis. We are particularly interested in non-stationarity adaptation [1] and density ratio estimation [2].
Literature
[1] Sugiyama, M. and Kawanabe, M. Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation, MIT Press, 2012.
[2] Sugiyama, M., Suzuki, T., and Kanamori, T. Density Ratio Estimation in Machine Learning, Cambridge University Press, 2012.
Other Activities
Institute of Electrical and Electronics Engineers (IEEE)
Institute of Electronics, Information and Communication Engineers of Japan (IEICE)
Information Processing Society of Japan (IPSJ)
The Japan Society for Industrial and Applied Mathematics (JSIAM)
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Future Plan
Our objective is to develop versatile machine learning technologies that enable non-experts to analyze data proficiently.
Messages to Students
Machine learning and statistical data analysis have a wide range of applications in science and engineering and are recently enjoying rapid progress and development. I hope students with a diverse background in mathematics, natural science, and engineering will join us in this exciting research field.
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