The creation of innovative data analysis methods with mathematical theoretical guarantees.

I have conducted research in pure mathematics, particularly on nonlinear partial differential equations that describe natural phenomena, using harmonic analysis. I obtained a Ph.D. in science in this field. At the same time, with the remarkable advancements in artificial intelligence technology based on deep learning, I could not go a day without hearing about its progress, which naturally led me to develop an interest in AI technology. Currently, I am engaged in research on the creation of innovative data analysis methods with theoretical guarantees by leveraging a wide range of mathematical disciplines, including machine learning, deep learning, neural networks, kernel methods, operator-theoretic data analysis, dynamical systems, differential equations, optimization problems, function space theory, functional analysis, C*-algebras, graph theory, and maximal monotone operator theory. In response to the demands of the data science field, I also sometimes focus on the theoretical development of these mathematical foundations themselves.