Masashi Sugiyama (Ph.D.)
Team Leader
2001Assistant Professor, Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology, Tokyo, Japan.
2003Associate Professor, Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology, Tokyo, Japan.
2014Professor, Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan. (Present)
2016Director, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan. (Present)


  • Team leader
    Masashi Sugiyama
  • Research scientist
    Gang Niu
  • Research scientist
    Fumiko Kawasaki
  • Research scientist
    Takahiro Mimori
  • Postdoctoral researcher
    Voot Tangkaratt
  • Postdoctoral researcher
    Jingfeng Zhang
  • Postdoctoral researcher
    Jiaqi Lyu
  • Postdoctoral researcher
    Shuo Chen
  • Technical scientist
    Yuka Mori
  • Technical staff I
    Masashi Ugawa
  • Senior visiting scientist
    Shinichi Nakajima
  • Visiting scientist
    Yoshihiro Nagano
  • Visiting scientist
    Florian Yger
  • Visiting scientist
    Takashi Ishida
  • Visiting scientist
    Miao Xu
  • Visiting scientist
    Takayuki Osa
  • Visiting scientist
    Bo Han
  • Visiting scientist
    Daichi Noborio
  • Visiting scientist
    Yuko Kuroki
  • Visiting scientist
    Hisashi Yoshida
  • Visiting scientist
    Ryohei Kasai
  • Visiting scientist
    Feng Liu
  • Visiting scientist
    Lei Feng
  • Visiting scientist
    Tongliang Liu
  • Junior research associate
    Takeshi Teshima
  • Junior research associate
    Yifan Zhang
  • Part-time worker I
    Zhenghang Cui
  • Part-time worker I
    Han Bao
  • Part-time worker I
    Masahiro Fujisawa


Recently, machine learning technology with big data has been actively investigated and its effectiveness has been demonstrated. However, depending on application domains, it is difficult or even it is not possible to collect a large amount of data. In the Imperfect Information Learning Team, for various machine learning tasks including supervised learning, unsupervised learning, and reinforcement learning, we develop novel algorithms that allow accurate learning from limited information. We also elucidate their theoretical properties and apply them to various real-world applications ranging from fundamental science to business.

Main Research Field
Computer Science
Research Field
Engineering / Mathematics
Research Subjects
Development of machine learning algorithms from imperfect information
Theoretical analysis of machine learning algorithms
Real-world application of machine learning algorithms
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