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Researcher's Profile

Associate Professor

Kiyoshi KOTANI

Photon based Advanced Manufacturing Science

E-mail: kotani.neuron.t.u-tokyo.ac.jp

Office:

Tel: 03-5452-5185

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Biography

2003.03
Dr.Eng., Graduate School of Engineering, The University of Tokyo (UTokyo)
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2003.04
Assistant Professor, Graduate School of Information Science and Technology, UTokyo
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2006.11
Lecturer, Graduate School of Frontier Science, UTokyo
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2011.09
Associate Professor, Graduate School of Frontier Science, UTokyo
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2014.04
Associate Professor, Graduate School of Engineering, UTokyo
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2014.08
Associate Professor, RCAST, UTokyo

Research Interests

Recent advances in experimental and analytical techniques have revealed that biological systems are precisely organized to do various functions better than we had imagined. We have been developing theories for dynamical systems and methods of measurement in order to elucidate the underlying mechanisms of complex biological phenomena. We also apply the basic biological findings to a wide range of fields, including diagnosis, rehabilitation, and human interfaces. Specifically, we have conducted studies on:
(a) Developing theoretical methods for nonlinear and time-delayed stochastic systems on complex networks, (b) Understanding the functions of working memory and recognition using multi-scale brain models and noninvasive brain measurements, and (c) High-speed brain-machine interfaces using virtual reality.
The main topics for our research group are as follows:

  • Mathematical theory for dynamical systems in biology
  • Dynamics of gene-regulatory networks with time-delayed interactions
  • Elucidating information processing in the brain using multi-scale brain models and noninvasive brain measurements
  • A signal processing method for precisely evaluating blood flow in the brain
  • Robotic ultrasound examinations to prevent lifestyle related diseases
  • A support system for manufacturing workers using non-invasive evaluation of the autonomic nervous system
  • Novel brain-machine interfaces based on augmented reality


Keywords

nonlinear dynamics, statistical physics, biomedical signal processing, human interface

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