For realizing next-generation information processing systems, it is indispensable to miniaturize devices and make structures compact for enhancement of energy efficiency. We aim to develop mathematical methodologies for making brain-like computing systems energy efficient such that efficient computing is realized with low power and high speed. For instance, we are conducting mathematical studies for algorithm design and hardware development based on reservoir computing as well as exploring efficient neural network architectures with less number of wiring cost.

Networked systems are ubiquitous in the world, such as the Internet, power networks, and biological networks. Networking often accompanies a risk that a partial failure causes a breakdown of the whole system. We are investigating how network robustness depends on network structure, dynamics, and element interactions. Our aim is to develop a design method of robust networks and a recovery method of damaged networks.

Machine learning technologies have enabled to efficiently perform tasks that have been manually handled by people. We aim to mathematically formulate problems in fields that are not approached by machine learning and mathematical modeling, and solve the problems by combining appropriate machine learning methods and advanced mathematical techniques. For instance, we are applying persistence homology and machine learning for structure prediction of organic crystals.

It is becoming possible to obtain real data on medical and social systems due to the developments of sensor devices and measurement techniques. We aim to propose effective control strategies for solving medically and socially important problems and improving quality of life. In particular, we have been studying mathematical modeling and analyses of atopic dermatitis, epidemics, prostate cancer, and cell signal transduction pathways.