UOsaka Machine Learning & Systems Laboratory -- HOME --

On the back of the digital transformation (DX) of social infrastructure and the strong interest in SDGs, the knowledge and technologies cultivated so far in the field of information science are in the phase of accelerating expansion to various scientific fields, industries and society in recent years. In particular, machine learning has become widely recognized as one of the most important fundamental information fields, which supports AI technologies rapidly pervading society, and is playing a indispensable role for the further development of increasingly sophisticated and diversified information infrastructure. In this laboratory, we will carry out basic study on new principles, algorithms, and models of machine learning, and at the same time, implement machine learning as a system and apply it to various fields while incorporating next-generation computing technology and software engineering that have been rapidly developing in recent years. Through the development and practice of information technology for this purpose, we will work on research activities and human resource development that contribute to solving problems in the increasingly large-scale and complex society and scientific fields, as well as wide-ranging information science.

We are recruiting students who are interested in machine learning and its related fields!

Visit for the information about Strucured Learning Team led by Prof. Kawahara in RIKEN AIP Center.

Recent News & Announcements

Asst. Prof. Fujisawa received the 2024 RIKEN Meihou Prize (March 17, 2025).
Dr. M. Fujisawa (Asst. Prof.) has joined our lab as a new member (March 3, 2025).
Dr. M. Ikeda (Asso. Prof.) and one administrative assistant have joined our lab as new members (February 1, 2025).
The paper on dynamical systems learning, "Learning Stochastic Nonlinear Dynamics with Embedded Latent Transfer Operators" (first-authored by N. Ke and R. Tanaka (Osaka University)), was accepted at the 28th Int'l Conf. on Artificial Intelligence and Statistics (AISTATS'25) (January 22, 2025).
The paper on reinforcement learning, "Self-supervised Color Generalization in Reinforcement Learning" (first-authored by M. Weissenbacher (RIKEN)), was accepted in Transactions on Machine Learning Research (October 28, 2024).
The paper on neural mechanisms incorporating dynamical structure, "Learning with Almost Invariant Sets in Neural Oscillatory ODEs" (first-authored by Y. Inui (Osaka University)), was accepted at the 31st Int'l Conf. on Neural Information Processing (ICONIP'24) (August 21, 2024).
The paper on extended Koopman analysis, "Enhancing Spectral Analysis in Nonlinear Dynamics: An Approach Based on Pseudoeigenfunctions from Continuous Spectra" (first-authored by Isushi Sakata (RIKEN)), was accepted in Scientific Reports (August 9, 2024).
The paper on anomaly detection applications, "Graph Deep Learning-based Anomaly Detection and Root Cause Estimation in Batteries" (first-authored by Joji Yoshikawa (Kyocera)), was accepted in the IEEJ Transactions (June 13, 2024).
The paper on online learning with Koopman spectra, "Koopman Spectrum Nonlinear Regulators and Efficient Online Learning" (first-authored by Motoya Onishi (University of Washington)), was accepted in Transactions on Machine Learning Research (May 22, 2024).
The paper on data analysis using dynamic mode decomposition, "Fast, accurate, and interpretable decoding of electrocorticographic signals using dynamic mode decomposition" (first-authored by Ryohei Fukuma (Osaka University)), was accepted in Communications Biology (May 6, 2024).
The paper on reinforcement learning, "SiT: Symmetry-invariant Transformers for Generalisation in Reinforcement Learning" (first-authored by M. Weissenbacher (RIKEN)), was accepted at the Int'l Conf. on Machine Learning (ICML'24) (May 2, 2024).
The paper on interpretable machine learning models, "MANet: Mixed Attention Network for Visual Explanation" (first-authored by Jingjing Bai (Kyushu University)), was accepted in New Generation Computing (February 22, 2024).
The paper on analyzing multi-agent behavior using machine learning, "Estimating counterfactual treatment outcomes over time in complex multi-agent scenarios" (first-authored by Keisuke Fujii (Nagoya University)), was accepted in IEEE Transactions on Neural Networks and Learning Systems (January 28, 2024).
The paper on EEG analysis using dynamic mode decomposition, "Manifold alteration between major depressive disorder and healthy control subjects using dynamic mode decomposition in resting-state fMRI data" (first-authored by Hidenori Endo (ATR)), was accepted in Frontiers in Psychiatry (January 15, 2024).
The paper on reinforcement learning-based modeling of multi-agent behavior, "Adaptive action supervision in reinforcement learning from real-world multi-agent demonstrations" (first-authored by Keisuke Fujii (Nagoya University)), was accepted at the 17th Int'l Conf. On Agents and Artificial Intelligence (ICAART'24) (December 8, 2023).
The paper on data-driven analysis of cellular automata using dynamic mode decomposition, "Dynamic mode decomposition for Koopman spectral analysis of elementary cellular automata" (first-authored by Keiri Taga (Waseda University)), was accepted in Chaos (December 1, 2023).
The paper on imitation learning for multi-agent behavior modeling, "Decentralized Policy Learning with Partial Observation and Mechanical Constraints for Multi-person Modeling Learning Systems" (first-authored by Keisuke Fujii (Nagoya University)), was accepted in Neural Networks (November 30, 2023).
Professor Shunji Umetani (Invited Professor) and Mr. Xiaoyu Yang (Special Postdoctoral Researcher) have joined our lab as new members (October 2, 2023).
The paper on tensor decomposition, "Many-body Approximation for Non-negative Tensors" (first-authored by Kazushi Garamkali (RIKEN AIP)), was accepted at the 26th Conf. on Neural Processing Systems (NeurIPS'23) (September 22, 2023).
The paper on anomaly detection, "A Characteristic Function for Shapley-Value-Based Attribution of Anomaly Scores" (first-authored by Naoya Takeishi (University of Tokyo)), was accepted in Transactions on Machine Learning Research (July 11, 2023).
The paper on statistical methods using reproducing kernels, "euMMD: Efficiently Computing the MMD Two-Sample Test Statistic for Univariate Data" (first-authored by D. Bodenham (Imperial College London)), was accepted in Statistics and Computing (June 22, 2023).
One research student has joined our lab as a new member (June 19, 2023).
The paper on deep equilibrium models (DEQ), "Stable Invariant Models with Koopman Spectra" (first-authored by Takuya Konishi), was accepted in Neural Networks (May 20, 2023).
Two administrative assistants and one research student have joined our lab as new members (May 1, 2023).
Four 4th-year undergraduate students from the Department of Information and Computer Sciences, School of Engineering Science, have joined our lab as new members (April 17, 2023).
Professor Kawahara has been appointed as a Domain Advisor for the JST ACT-X program "Innovative Mathematical and Information Science for the Next-Generation AI" (April 11, 2023).
One specially appointed lecturer, one doctoral student, and four special research students have joined our lab as new members (April 3, 2023).

The University of Osaka
Machine Learning & Systems Laboratory

Graduate School of Information Science and Technology

Faculty Members

Yoshinobu Kawahara

Masahiro Ikeda

Takuya Konishi

Masahiro Fujisawa