Mathematical Informatics

Research Staff

  • Prof. Kazushi Ikeda

    Kazushi IKEDA

  • Assoc.Prof. Junichiro Yoshimoto

    Junichiro YOSHIMOTO

  • Assoc.Prof. Takatomi Kubo

    Takatomi KUBO

  • Assist.Prof. Makoto Fukushima

    Makoto FUKUSHIMA

  • Assist.Prof. Chie Hieida

    Chie HIEIDA


    Renzo Roel Perez TAN

E-mail {kazushi, juniti-y, takatomi-k, mfukushi, hieida, rr.tan }[at]

Research Areas

We study mathematical models for life sciences, from cell biology and neuroscience to medical science and social interaction. Our interdisciplinary research covers computation (machine learning), science (mathematical biology) and engineering (signal processing).

Machine learning

Statistical learning theory

Statistical signal processing based on Bayes theory

Neural network theory

Information geometry and information theory

Factor analysis and sparse models

Reinforcement learning theory and applications

Mathematical biology

Math models for cell biology

Modeling and medical decision support for neuropsychiatric discorders

Neural mechanism of empathy

Behavior analysis using smart sensors

Cognitive interaction design and social interaction

Signal processing

Advanced driver assistance systems

Adaptive signal processing theory and application

Non-invasive human-machine interfaces

Anomaly diagnosis by big-data analysis

Deep learning methods and application

Key Features

Mathematical informatics is interdisciplinary; faculty and students in our lab have a variety of backgrounds, such as mathematical engineering, electric and electronic engineering, mechano-informatics, statistical science, physics, psychology, social science and medical science. We welcome students from any background since "mathematical models are everywhere", as long as they are interested in mathematical models.

Fig.1 Mathematical models in Computation

Fig.1 Mathematical models in computation

Fig.2 Mathematical models in Science

Fig.2 Mathematical models in science

Fig.3 Mathematical models in Engineering

Fig.3 Mathematical models in engineering