Staff Introduction
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今井 哲郎 Tetsuo IMAI
- Emailimainagasaki-u.ac.jp- Position / Degree Institute of Integrated Science and Technology, Associate Professor
School of Information and Data Sciences, Associate Professor
Doctor of Engineering- Specialized Field Network science, Complex systems, Machine Learning, Internet of Things (IoT)
CV
| Mar.2000 | Bachelor of Engineering in Computer Science, Hokkaido University |
| Mar.2002 | Master of Engineering in Systems and Information Engineering, Hokkaido University |
| Apr.2002 | NEC Corporation, Networking Research Laboratories |
| Jan.2004 | NEC Corporation, System Platforms Research Laboratories |
| Apr.2006 | Information Synergy Center, Tohoku University |
| Sep.2012 | Ph.D. in Engineering, Yamagata University |
| Oct.2012 | Research Fellow, Yamagata University |
| Mar.2013 | Postdoctoral Researcher, RIKEN Advanced Institute for Computational Science (AICS) |
| Apr.2015 | Assistant Professor, Department of Informatics, Tokyo University of Information Sciences |
| Apr.2017 | Postdoctoral Researcher, Telenursing Research Center, Tokyo University of Information Sciences |
| Sep.2018 | Assistant Professor, Graduate School of Engineering, Nagasaki University |
| Apr.2021 | Lecturer, Graduate School of Information Sciences, Hiroshima City University |
| Apr.2024 | Associate Professor, School of Information and Data Sciences, Nagasaki University |
Research Activities
Network Science and its Applications
Inducing to better social networks under the COVID-19 pandemic
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- Restrictions on human contact are effective in controlling the spread of infectious diseases, yet they also impose significant restrictions on socio-economic activities.
- We are researching methods to obtain a social network structure that moderately balances infection control and socio-economic activities using a network formation model based on game theory.
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Resolving regional business issues through the IoT and AI
Anomaly detection in knitting machines
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- Continuous monitoring by cameras on factory production lines
- Automatic product anomaly detection based on machine learning
- Toward predictive detection of anomalies へ
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Educational Activities
Class
Information and Data Sciences： First-Year Seminar, Information Network I, Information Network II, Research Project