Education

April 2014 - March 2015Ph.D. in the field of Computer Science and Information Technology
Graduate School of Information Science and Technology, Hokkaido University, Japan
April 2012 - March 2014Master of Computer Science and Information Technology
Graduate School of Information Science and Technology, Hokkaido University, Japan
April 2008 - March 2012Bachelor of Engineering
School of Enginnering, Hokkaido University, Japan

Work Experience

April 2017 - presentAssistant Professor
School of Computing, Tokyo Institute of Technology, Japan
March 2016 - February 2017Visiting Postdoctoral Researcher
IRIDIA, Université Libre de Bruxelles, Belgium
April 2015 - March 2017Research Fellow (PD) of the Japan Society for the Promotion of Science (JSPS)
Graduate School of Information Science and Technology, Hokkaido University, Japan
April 2014 - March 2015JSPS Research Fellow (DC1)
Graduate School of Information Science and Technology, Hokkaido University, Japan

Grants

April 2017 - (March 2021)Grant-in-Aid for Young Scientists (B): 3,500,000 JPY
17K12734: "Swarm Learning for Deep Neural Networks"
April 2015 - March 2017Grant-in-Aid for JSPS Fellow (DC1, PD): 2,500,000 JPY
26-1342: "Rule-based Neighborhood Search for Large Scale Scheduling Problem"

Project

Collaborative Learning based on Collective Decision Making

April 2017 - present, Tokyo Institute of Technology (Japan)

Keywords: Swarm Intelligence, Deep Neural Networks, Collaborative Learning

TBU

This project is supported by JSPS KAKENHI, Grant-in-Aid for Young Scientists (B), No. 17K12734, from April 2017.

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Kilogrid (Collaboration)

March 2016 - February 2017, Université Libre de Bruxelles (Belgium)

Keywords: Swarm Intelligence, Swarm Robotics, Collective Decision Making

Kilogrid is a modular virtualization environment for the kilobot robots. It is developped under Prof. Marco Dorigo's supervision at IRIDIA, Université Libre de Bruxelles.

Link: Official project page

Picture of Kilogrid

Dynamic Rule Applied Metaheuristics for Large Scale Scheduling Problems

April 2012 - March 2017, Hokkaido University (Japan)

Keywords: Combinatorial Optimization, Heuristics, Metaheuristics, Scheduling

This project proposed several solution algorithms for scheduling problems. The algorithms are based on meta-heuristics and classical heuristic rules which also optimized dynamically while searching for candidate solutions.

This project was supported by JSPS KAKENHI, Grant-in-Aid for JSPS Fellows, No. 26-1342, from April 2014 to March 2017.

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Publication

Selected Papers

  1. Yasumasa Tamura, Hiroyuki Iizuka, Masahito Yamamoto, Masashi Furukawa: Application of Local Clustering Organization to Reactive Job-shop Scheduling, Soft Computing, Springer, Vol. 19, Issue 4, pp. 891-899, 2015.
  2. Yasumasa Tamura, Masahito Yamamoto, Ikuo Suzuki, Masashi Furukawa: Acquisition of Dispatching Rules for Job-shop Scheduling Problem by Artificial Neural Network with PSO, Journal of Advanced Computational Intelligence and Intelligent Informatics, Fuji tech. press, Vol. 17, No. 5, pp. 731-738, 2013.
  3. Yasumasa Tamura, Ikuo Suzuki, Masahito Yamamoto, Masashi Furukawa: The Hybrid Approach of LCO and SA to Solve Job-shop Scheduling Problem, Transactions of the Institute of Systems, Control and Information Engineers, Institute of Systems, Control and Information Engineers, Vol. 26, No. 4, pp. 121-128, 2013.

International Conferences

  1. Yasumasa Tamura, Xavier Défago: Collective Learning with Deep Neural Networks, Proceeding of the 2nd International Symposium on Swarm Behavior and Bio-Inspired Robotics (SWARM2017), pp. 167-168, Kyoto (Japan), 2017.
  2. Yasumasa Tamura, Hiroyuki Iizuka, Masahito Yamamoto: Extended Local Clustering Organization using Rule-Based Neighborhood Search for Job-shop Scheduling Problem, Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems, Vol. 2, pp. 465-477, Singapore, 2014.
  3. Yasumasa Tamura, Hiroyuki Iizuka, Masahito Yamamoto, Masashi Furukawa: Application of Local Clustering Organization to Reactive Job-shop Scheduling, Proceedings of the 14th International Symposium on Advanced Intelligent Systems (ISIS 2013), T1f-4, Deajeon (South Korea), 2013.
  4. Yasumasa Tamura, Ikuo Suzuki, Masahito Yamamoto, Masashi Furukawa: The Hybrid Approach of LCO and SA to Solve Job-shop Scheduling Problem, Proceedings of the ASME/ISCIE 2012 International Symposium on Flexible Automation (ISFA 2012), 7226, St. Louis (MO, USA), 2012.

Theses

  1. Yasumasa Tamura: Studies on Advanced Metaheuristics employing Local Clustering for Job-shop Scheduling Problem, Ph.D. (Computer Science), Hokkaido University, 2015.
  2. Yasumasa Tamura: Learning of Dispatching Rules and Its Application to Searching Algorithms for Job-shop Scheduling Problem, Master of Computer Science, Hokkaido University, 2014. (Japanese)

Contact

tamura(at)c.titech.ac.jp
+81-(0)3-5734-2772
+81-(0)3-5734-2772
W8-84, 2-12-1 Ookayama, Meguro-ku, Tokyo
152-8550, Japan
(Office: West Bldg. 8E, 8th Floor, Room E804)