Mathematical Informatics

Research Staff

  • Professor Kazushi IKEDA

    Professor
    Kazushi IKEDA

  • Affiliate　Professor Junichiro YOSHIMOTO

    Affiliate Professor
    Junichiro YOSHIMOTO

  • Affiliate　Professor Toshitaka YAMAKAWA

    Affiliate Professor
    Toshitaka YAMAKAWA

  • Associate Professor Takatomi KUBO

    Associate Professor
    Takatomi KUBO

  • Associate Professor Tomoya TAMEI

    Affiliate Associate Professor
    Tomoya TAMEI

  • Assistant Professor Yuzhe LI

    Assistant Professor
    Yuzhe LI

  • Assistant Professor Chie HIEIDA

    Assistant Professor
    Chie HIEIDA

  • Assistant Professor Renzo Roel Perez TAN

    Assistant Professor
    Renzo Roel Perez TAN

E-mail kazushi@is.naist.jp
juniti-y@is.naist.jp
takatomi-k@is.naist.jp
yuzhe.li@naist.ac.jp
hieida@is.naist.jp
rr.tan@is.naist.jp
To the site https://sites.google.com/view/milab/

Research Areas

Fig.1 Mathematical models in computation
 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.2 Mathematical models in science

Fig.3 Mathematical models in engineering
 Fig.3 Mathematical models in engineering

Fig.3 Mathematical models in engineering

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.