Mathematical Modeling and Data Analysis Based on Reduction Theory

My research focuses on modeling diverse and complex dynamical phenomena observed in the real world within the frameworks of dynamical systems theory and stochastic processes. In particular, I aim to understand and predict such phenomena by extracting essential structures from high-dimensional and complex data and constructing low-dimensional reduced models that can be represented with a small number of degrees of freedom. In addition to establishing theoretical foundations for deriving reduced models, I also engage in mathematical modeling and data analysis as practical applications of these theories.
One of the most successful applications of my theoretical approach is the study of rhythmic coordination mechanisms underlying human rhythm and tempo control in music performance and motor behavior. I have proposed reduced models that describe human rhythmic coordination and developed methods for estimating model parameters by applying them to behavioral experiment data. This approach is contributing to the elucidation of mechanisms behind seemingly nontrivial phenomena, such as tempo acceleration.