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.


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: textnormal{makoto.yamada@riken.jp}


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.


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