Naoya Takeishi
(武石 直也)

Naoya Takeishi is a researcher working at the Data Mining and Machine Learning Group of the University of Applied Sciences and Arts Western Switzerland (HES-SO), Geneva. He is also a visiting scientist at the RIKEN Center for Advanced Intelligence Project (AIP), Japan. He received his Ph.D. in Engineering from the Department of Aeronautics and Astronautics, the University of Tokyo in 2018. He is interested in effective integration of domain knowledge into statistical machine learning and also in data-driven analysis of dynamical systems.

Research Interests

Knowledge-informed ML.  Effective integration of domain-specific prior knowledge / inductive bias (e.g., physical relations, simulators, logical rules from expert's domain knowledge, and side information) into statistical machine learning.

Data-driven analysis of dynamical systems.  Analysis of dynamical systems and time-series data based on the operator-theoretic view and its data-driven method, such as dynamic mode decomposition (DMD).

Anomaly detection.  Application of anomaly detection techniques based on machine learning to engineering systems, such as artificial satellites, vehicles, and power plants. Methodology for explanation of anomaly detection.

Visual SLAM in space.  Simultaneous estimation of the shape and motion of a target celestial body (e.g., asteroid) as well as the position and attitude of a spacecraft.


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