Makoto Yamada, Ph.D. [Japanese/English]

Makoto Yamada 

Makoto Yamada, Ph.D.
Associate Professor, Kyoto University
Unit Leader (PI), High-dimensional Statistical Modeling Unit, RIKEN AIP
Visiting Associate Professor, Research Center for Statistical Machine Learning, Institute of Statistical Mathematics
Visiting Associate Professor, Department of Computer Science, Aalto University

E-mail: textnormal{}


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 students. Please feel free to send me your resume!


2018 Will serve as a Senior PC member of WSDM 2018 and IJCAI 2018.
2018 Will serve as a Publicity chair of WSDM 2018.
2018/04/1 I have started working at Kyoto University as an associate professor.
2018/02/21 We uploaded our recent work about MMD based inference algorithms at arXiv Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimator, Selecting the Best in GANs Family: a Post Selection Inference Framework.
2018/02/11 We uploaded our recent work about topological data analysis at arXiv Riemannian Manifold Kernel for Persistence Diagrams.
2017/12/24 Our paper entitled “Ultra High-Dimensional Nonlinear Feature Selection for Big Biological Data” has been accepted to IEEE Transactions on Knowledge and Data Engineering.
2017/12/23 Our paper entitled Post selection inference with kernels has been accepted to AISTATS 2018.
2017/12/18 Denny Wu (CMU) and Yao-Hung Hubert Tsai will stay our unit for one month.
2017/12/8 We will present our work Post Selection Inference with MMD at Learning on Distributions, Functions, Graphs and Groups NIPS 2017.
2017/11/16 We uploaded our recent work at arXiv Deep Matching Autoencoders.
2017/10/13 Dr. Sujith Ravi and Dr. Zornitsa Kozareva stayed our unit for a week.

Current Research Interests

Machine Learning

  • Nonlinear high-dimensional feature selection

  • Ultra-high dimensional Feature selection

  • Change point detection