Masashi Tsubaki, Ph.D.

I am a researcher with the Machine Learning Research Team of the Artificial Intelligence Research Center (AIRC) at National Institute of Advanced Industrial Science and Technology (AIST), Japan.

I am interested in the physically-informed machine learning applications for biology, chemistry, and physics. In particular, my current focus is the deep learning, quantum chemistry, and density functional theory.

Email: x@aist.go.jp, where x = tsubaki.masashi, or y@gmail.com, where y = masashi.tsubaki.814

GitHub: https://github.com/masashitsubaki


Comparing subject-to-subject transfer learning methods in surface electromyogram-based motion recognition with shallow and deep classifiers [paper]

Takayuki Hoshino, Suguru Kanoga, Masashi Tsubaki, and Atsushi Aoyama

Neurocomputing, 2021


Quantum deep descriptor: Physically informed transfer learning from small molecules to polymers [paper]

Masashi Tsubaki and Teruyasu Mizoguchi

Journal of Chemical Theory and Computation, 2021


Quantum deep field: data-driven wave function, electron density generation, and energy prediction and extrapolation with machine learning [paper] [code]

Masashi Tsubaki and Teruyasu Mizoguchi

Physical Review Letters, 2020


On the equivalence of molecular graph convolution and molecular wave function with poor basis set [paper] [code]

Masashi Tsubaki and Teruyasu Mizoguchi

Advances in Neural Information Processing Systems (NeurIPS 2020)


Analysis and usage: subject-to-subject linear domain adaptation in sEMG classification [paper]

Takayuki Hoshino, Suguru Kanoga, Masashi Tsubaki, and Atsushi Aoyama

42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2020)


Learning excited states from ground states by using an artificial neural network [paper]

Shin Kiyohara, Masashi Tsubaki, and Teruyasu Mizoguchi

npj Computational Materials, 2020


Dual graph convolutional neural network for predicting chemical networks [paper]

Shonosuke Harada, Hirotaka Akita, Masashi Tsubaki, Yukino Baba, Ichigaku Takigawa, Yoshihiro Yamanishi, and Hisashi Kashima

BMC Bioinformatics, 2020


Uncovering prognosis-related genes and pathways by multi-omics analysis in lung cancer [paper]

Ken Asada, Kazuma Kobayashi, Samuel Joutard, Masashi Tsubaki, Satoshi Takahashi, Ken Takasawa, Masaaki Komatsu, Syuzo Kaneko, Jun Sese, and Ryuji Hamamoto

Biomolecules, 2020


Quantitative estimation of properties from core-loss spectrum via neural network [paper]

Shin Kiyohara, Masashi Tsubaki, Kunyen Liao, and Teruyasu Mizoguchi

Journal of Physics: Materials, 2019


Mean-field theory of graph neural networks in graph partitioning [paper]

Tatsuro Kawamoto, Masashi Tsubaki, and Tomoyuki Obuchi

Advances in Neural Information Processing Systems (NIPS 2018)


Fast and accurate molecular property prediction: learning atomic interactions and potentials with neural networks [paper] [code] [errata]

Masashi Tsubaki and Teruyasu Mizoguchi

The Journal of Physical Chemistry Letters, 2018


Compound-protein interaction prediction with end-to-end learning of neural networks for graphs and sequences [paper] [code1] [code2]

Masashi Tsubaki, Kentaro Tomii, and Jun Sese

Bioinformatics, 2018