About

Work and Education

2008. B.E., Department of Materials Engineering, Faculty of Engineering, The University of Tokyo
2010.  Master of Information Science and Technology, Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo
2011. JSPS fellow（DC2）
2011. Department of Statistics, Purdue University, Visiting scholar
2013. Ph.D. (Information Science and Technology), Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo 2013. JSPS fellow（PD）
2013. Assistant Professor, Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo
2018. Full-time Lecturer, Department of General Systems Studies, Graduate School of Arts and Sciences, The University of Tokyo
2020. Associate Professor, Center for Education and Research in Information Science and Technology (CERIST), The University of Tokyo
2022. Current position

Docter Student

Masatoshi Kobayashi (Department of  Interdisciplinary Science, College of Arts and Sciences)

Master Student

Yuma Takeda (Department of  Interdisciplinary Science, College of Arts and Sciences)

Bachelor Student

Yuta Kuboki (Department of  Interdisciplinary Science, College of Arts and Sciences)

Alumni

Masaki Kozuki (Department of General Systems Studies, Graduate School of Arts of Sciences)
Shota Hayashi (Department of General Systems Studies, Graduate School of Arts of Sciences)
Takuto Kimura (Department of Creative Informatics, Graduate School of Information Science and Technology)
Ziyu Guo (Department of Creative Informatics, Graduate School of Information Science and Technology)
Hiroya Iyori (Department of Creative Informatics, Graduate School of Information Science and Technology)
Han Zhou (Department of Creative Informatics, Graduate School of Information Science and Technology)
Hiroki Nishimoto (Department of General Systems Studies, Graduate School of Arts of Sciences)
Kota Misaki (Department of General Systems Studies, Graduate School of Arts of Sciences)

Master and PhD positions

Our laboratory focuses on both the practical application and fundamental theory of machine learning. Our key approach is to develop efficient algorithms that can easily be used by anybody. As for research into the theory, we evaluate statistical properties of machine learning methods and the efficiency of learning algorithms. We do this via statistical learning and optimization theory with a view towards developing methods that guarantee favorable behaviors in the real world. As for research into the applications, we develop methods and algorithms tailored to the properties and structure of real world data with the goal of knowledge discovery and value creation.

keywords: explainable models / statistical causal inference/ knowledge discovery / large-scale learning / sparse learning / subspace clustering / asynchronous optimization

Our group accepts students at the department of general systems studies in the graduate school of arts and sciences; information for the application can be found here. Please feel free to contact us through the contact form for lab visiting, possible research topics, personal career plans, etc.