Machine Learning and Data Science (MLDS) Unit

In machine learning and data science unit, we focus on developing fundamental machine learning algorithms and solving important scientific problems using machine learning. We are currently interested in statistical modeling for high-dimensional data including kernel and deep learning models and geometric machine learning algorithms, including graph neural networks (GNN) and optimal transport problems. In addition to developing ML models, we focus on developing new machine learning methods to find a new scientific discoveries from data automatically.

Join us!

We are looking for start-up members for our newly created Machine Learning and Data Science Unit. We look forward to hearing from you soon!

Informaiton about the MLDS unit at OIST:

  • We are part of OIST, Japan’s international and interdisciplinary research institution and is very strong in natural science research. Therefore, you can not only conduct fundamental machine learning research but also research on the application of machine learning to the natural sciences (i.e., Machine Learning for Science).
  • We are an international team: both our members and collaborators are from all over the world.
  • We regularly publish papers at top ML venues, including NeurIPS, ICML, AISTATS, and ICLR.
  • We are closely working with High-dimensional statistical modeling team at RIKEN AIP  and the Kashima-Yamada labs at Kyoto University. We collaborated with researchers at CMU, Oxford, and UCL. Moreover, we actively collaborated with industries, including NTT communication science laboratory, CyberAgent, Google Brain, Meta AI, and SliceX AI. 


We will look for researchers soon!

Ph.D. students

Please directly apply to the OIST's Ph.D. program.
If you are willing to join OIST through the pathway program, please consider applying to the OIST internship programs.

Research interns

Please directly apply to the OIST's internship program.
We may have openings for unit interns for M.S. or Ph.D. students. If you are interested in the opportunity, please send us your CV, published papers and/or technical report (arXiv), and a short research statement to makoto.yamada (at)


We can accept visitors at the beautiful OIST campus after 2023/April.