Naoya Takeishi is a researcher working at the Data Mining and Machine Learning Group of Haute école spécialisée de Suisse occidentale (HES-SO) Genève, Switzerland. He is interested in the effective integration of domain knowledge into statistical machine learning and also in data-driven analysis of dynamical systems, whose application ranges from scientific research to industrial machinery.

## Contact

naoya.takeishi[at]hesge.ch **or** naoya.takeishi[at]a.riken.jp

Old addresses (…@riken.jp, …@ailab.t.u-tokyo.ac.jp) are not observed anymore!

## Research interests

### Knowledge-augmented machine learning

Effective integration of domain-specific prior knowledge / inductive bias (e.g., mathematical models of phenomena, simulators, logical rules, and side information) into statistical machine learning. For example, we developed a deep dynamics model with prior knowledge of stable invariant sets such as limit cycles. We formulated a method for learning dynamical systems with side information. We proposed a regularized learning method for properly learning a deep grey-box models, which are useful for robust and physically-interpretable generative modeling. We further discussed the learning methods of such models. Also interested in the simulation-based inference as a related topic.

### Data-driven analysis of dynamical systems

Analysis of dynamical systems based on data-driven methods, such as dynamic mode decomposition (DMD) and its variants. We proposed, e.g., Bayesian DMD, DMD for random dynamics, DMD with neural net observables, nonnegative DMD, time-varying DMD, and discriminant DMD for labeled time-series. We introduced the use of multiple kernel learning for diffusion-map-based Koopman analysis.

### Data-driven prognostics and health management

Application of anomaly detection techniques based on machine learning to engineering systems, such as artificial satellites, vehicles, and power plants, as well as methodology for interpretation of anomaly detection. For example, we investigated the use of Shapley values for interpreting semi-supervised anomaly detection.