High-Dimensional Statistical Modeling Unit (RIKEN AIP)

We are focusing on developing machine learning algorithm for science including feature selection, topological data analysis, and transfer learning. In particular, we are interested in nonlinear algorithms for high-dimensional data. Ultimately, we want to build a machine learning framework to automatically find an important information from data.

Hiring

Intern/visitors
Paid intern positions are available for motivated M.S. or Ph.D. students. Please feel free to send me your resume!

Ph.D. student
We accept graduate students from Graduate School of Informatics, Kyoto University.

E-mail:

News

2019/11/11 One paper has been accepted to AAAI 2020.
2019/09/04 Three papers have been accepted to NeurIPS 2019.
2019/08/12 Our kernel based attention paper has been accepted to EMNLP-IJCNLP 2019.
2019/04/22 Tam's safe screening paper has been accepted to ICML 2019.
2019/04/10 Our nonlinear feature selection paper has been accepted to ISMB/ECCB 2019.
2019/04/05 Our Graph Neural Network paper has been accepted to ICLR workshop.
2018/12/21 Our post selection inference paper has been accepted to ICLR 2019.

Access

RIKEN Kyoto Office
Research Bldg. No.15
Kyoto University
Yoshida-honmachi, Sakyo-ku, Kyoto
606-8501
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