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.


Postdoctoral fellows/Research Scientist
Several positions for machine learning and its application to biology and material informatics, etc. We look for a couple of machine learning researchers developing machine learning algorithms and a couple of researchers in application domain.

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}


2018/12/21 Our post selection inference paper has been accepted to ICLR 2019.
2018/11/14 Our robust feature selection paper has been accepted to Journal of Computational Biology.
2018/09/17 Serve as an Area chair of ICML 2019.
2018/09/5 Our topological data analysis paper (First author Tam Le) accepted to NIPS 2018.
2018/08/28 Our intelligent cell sorting paper has been accepted to Cell, Article1.
2018/08/28 Our unsupervised word translation paper has been accepted to EMNLP 2018.
2018/08/14 Our domain adaptation for wearable device has been accepted to ISWC 2018.
2018/04/1 Makoto Yamada has started working at Kyoto University as an associate professor.


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