Masashi Sugiyama

Director
RIKEN Center for Advanced Intelligence Project.

Professor
Department of Complexity Science and Engineering,
Graduate School of Frontier Sciences,
The University of Tokyo.

Biography (short)

Masashi Sugiyama received a Ph.D. degree in Computer Science from Tokyo Institute of Technology, Japan, in 2001. After experiencing assistant and associate professors at the same institute, he became a professor at the University of Tokyo in 2014. Since 2016, he has concurrently served as Director of the RIKEN Center for Advanced Intelligence Project. His research interests include theories and algorithms of machine learning. He was a recipient of the Japan Academy Medal in 2017 and the Commendation for Science and Technology by the Minister of Education, Culture, Sports, Science and Technology in Japan in 2022.

Biography (long)

Masashi Sugiyama was born in Osaka, Japan, in 1974. He received a Bachelor of Engineering, Master of Engineering, and Doctor of Engineering in Computer Science from Tokyo Institute of Technology, Japan, in 1997, 1999, and 2001. In 2001, he was appointed Assistant Professor in the same institute, and he was promoted to Associate Professor in 2003. He moved to the University of Tokyo as Professor in 2014. Since 2016, he has concurrently served as Director of the RIKEN Center for Advanced Intelligence Project, leading the groups of fundamental AI technologies, AI applications, and social issues of AI. He received an Alexander von Humboldt Foundation Research Fellowship and researched at Fraunhofer Institute, Berlin, Germany, from 2003 to 2004. In 2006, he received European Commission Program Erasmus Mundus Scholarship and researched at the University of Edinburgh, Edinburgh, UK.

His research interests include theories and algorithms of machine learning and statistical data analysis. He (co)-authored various machine learning monographs, including Machine Learning in Non-Stationary Environments (MIT Press, 2012), Density Ratio Estimation in Machine Learning (Cambridge University Press, 2012), Statistical Reinforcement Learning (Chapman & Hall, 2015), Introduction to Statistical Machine Learning (Morgan Kaufmann, 2015), Variational Bayesian Learning Theory (Cambridge University Press, 2019), and Machine Learning from Weak Supervision (MIT Press, 2022). He served as a Program Chair for ACML2010, NeurIPS2015, AISTATS2019, and ACML2020.

He received the Faculty Award from IBM in 2007 for his contribution to machine learning under non-stationarity, the Nagao Special Researcher Award from the Information Processing Society of Japan in 2011, and the Young Scientists' Prize for the Commendation for Science and Technology by the Minister of Education, Culture, Sports, Science and Technology Japan in 2014 for his contribution to the density-ratio paradigm of machine learning, and the Japan Society for the Promotion of Science Award and the Japan Academy Medal in 2017 for his series of machine learning research. In 2022, he was awarded the Commendation for Science and Technology by the Minister of Education, Culture, Sports, Science and Technology in Japan for his research on weakly supervised machine learning.

Masashi Sugiyama (sugi [at] k.u-tokyo.ac.jp)