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 deep learning applications for biology, chemistry, and physics.

Email:, where x = tsubaki.masashi or, where y = masashi.tsubaki.814

Publications (Google Scholar)

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.

End-to-end Learning of Graph Neural Networks for Latent Molecular Representations [poster],

Masashi Tsubaki, Masashi Shimbo, Atsunori Kanemura, and Hideki Asoh,

Advances in Neural Information Processing Systems (NIPS 2017) Workshop, Machine Learning for Molecules and Materials,

Best paper award.

Protein Fold Recognition with Representation Learning and Long Short-Term Memory [paper],

Masashi Tsubaki, Masashi Shimbo, and Yuji Matsumoto,

IPSJ Transactions on Bioinformatics, 2017.

Non-linear Similarity Learning for Compositionality [paper],

Masashi Tsubaki, Kevin Duh, Masashi Shimbo, and Yuji Matsumoto,

The thirtieth AAAI Conference on Artificial Intelligence (AAAI 2016).

Modeling and Learning Semantic Co-Compositionality through Prototype Projections and Neural Networks [paper],

Masashi Tsubaki, Kevin Duh, Masashi Shimbo, and Yuji Matsumoto,

Conference on Empirical Methods in Natural Language Processing (EMNLP 2013).