Shintaro Fukushima

Shintaro Fukushima

  • Machine learning
    Data mining
    Materials informatics

Mathematical theory and computers have been important domains to me ever since I was a graduate student. Recently, I have been conducting research and development on machine learning, data mining and AI. I try to appreciate on-premise issues and the characteristics of the data, bearing in mind that the solutions will be used in real society, avoiding to focus solely on theories and algorithms.

  • Department

    • Connected Company
      Group Manager / Principal Researcher, Data Analysis Infrastructure Group,
      Infotech,
      Connected Advanced Development Div.
  • Biography

    • Ph.D. (Information Science and Technology)

    • 2015-

      Engaged in research and development on analysis of automobile driving history data, quality assurance and management of machine learning, materials informatics, detecting abnormalities, detecting changes, and causal inference with factory data, behavior prediction using images of vehicle staffs, and other areas

    • 2017-
      2020

      Engaged in research on machine learning and data mining in his Ph.D. course at The University of Tokyo (change detection and its early signal detection)

    • 2010-
      2014

      Conducted research, development, consulting and business planning associated with machine learning, data mining and services and platforms for analyzing big data for a wide range of areas including manufacturing, finance, Web and medicine, at an electronics manufacturer and think tank

    • 2006-
      2009

      Engaged in research and development on financial engineering at a think tank (built models for derivative pricing and risk measurement)

    • 2000-
      2006

      Majored in physics and applied mathematics (non-linear mechanics, chaos theory and numerical computations) as an undergraduate and graduate student at The University of Tokyo

  • Publication List/Conference presentation

    Paper

    • Estimating Total Traffic Volume with Statistical Modeling Approach, IEEE, ITSC 2022, 2022

    • Graph summarization with latent variable probabilistic models, The 10th International Conference on Complex Networks and their Applications (Complex Networks2021), Springer, pp. 428-440, 2021

    • Detecting hierarchical changes in latent variable models, In Proceedings of 2020 IEEE International Conference on Data Mining (IEEE ICDM2020), pp.1028-1033, 2020

    • Detecting metachanges in data streams from the viewpoint of the MDL principle, Entropy, 21(12), 1134, 2019

    • Model change detection with the MDL principle, IEEE Transactions on Information Theory, 64(9), pp. 6115 - 6126, 2018

    • “Numerical calculation of topological entropy using turning points of a curve transformed by a map.” IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences (Japanese Edition), Vol. J90-A No.12, pp.932-939,2007

    • etc.

    Books and Articles

    • Human Resource Development for Data Utilization in Connected Domain, TECHNICAL INFORMATION INSTITUTE CO.,LYD., Research and development leader, 2022

    • (Supervision of Japanese translation) Python Machine Learning, 3rd Edition. Impress, 2020

    • “Why Python is the Best for Machine Learning.” Software Design, June 2017 issue, 2017

    • Data Scientist Learning Handbook—Introduction to Machine Learning. Gijutsu-Hyohron, 2015

    • Data Analysis Process. Kyoritsu Shuppan, 2015

    • High-Performance Computing with R. Socym, 2014

    • etc.

    Invited Conference presentation / Lecture

    • Trends and activities of quality management and assurance of AI and machine learning, TECHNICAL INFORMATION INSTITUTE CO.,LYD., Seminar "Ethical Problems of AI(Artificial Intelligence) and Necessary Actions of Companies", 2022

    • A once-in-a-century period of great change: Data analysis and outlook in the connected domain, Graduate School of Information Science and Technology, DSS Symposium, 2022

    • Vehicle data analysis and utilization in Toyota Motor Corporation, Graduate school lecture: Applied Artificial Intelligence and Data Science D,, School of Computing Department of Computer Science,, Tokyo Institute of Technology, 2022

    • Trends and Activities of Quality Management and Assurance of Machine Learning, DBSJ Seminar, The Database Society of Japan, 2022

    • Important points and use cases in data analysis in TOYOTA, DataRobot Japan Inc., DataRobot Japan Seminar, 2021

    • Key points and case studies in data analysis in TOYOTA, Nagoya University, Mathematical and Data Science Center, Practical Data Scientist Training Program, 2021

    • Trends and activities in materials search and retrosynthetic planning, Materials Informatics Seminar, 2021

    • Trends and Activities of Quality Management and Assurance of Machine Learning, National Institute of Informatics (NII) AI/Iot system symposium executive committee et al., The 2nd AI / IoT System Safety and Security Symposium(AIS^3), 2020

    • Trends and activities in quality management and assurance of machine learning, PyData.Tokyo, 2020

    • “Prospects in Machine Learning Application—Trends and Technologies of AI Quality Assurance,” guest lecture at AI Management Conference, Nikon Corporation, 2019

    • Quality Assurance of Machine Learning AI, CEATEC 2018

    • Materials Informatics and Python -Intesection of condensed matter physics and material science, and data science-, PyData.Tokyo One Day Conference, 2018

    • An Introduction to Machine Learning, Invited talk in Financial dealings and the market economy(2), Graduate School of Commerce, Chuo University, 2018

    • Ideal Image and Development of Human Resources for Data Utilization, guest lecture at 5th Biannual Conference of Transdisciplinary Federation of Science and Technology (Oukan Conference), 2014

    • etc.

  • Commendation/Memberships

    • Visiting Researcher, The University of Tokyo

    • Visiting Researcher, National Institute of Advanced Industrial Science and Technology (AIST)

    • Committee Member, AI Quality Management Study Committee, Research and Development on Quality Assurance of Machine Learning AI (under the advanced research themes aimed at social implementation of next-generation artificial intelligence technology by the New Energy and Industrial Technology Development Organization and the National Institute of Advanced Industrial Science and Technology)
      Participation Researcher, National Institute of Informatics (“Quality Assurance of Machine Learning-based Systems (JST-QAML),” JST-Mirai Program)

    • Editorial Committee Member of Academic Journal in The Japan Society for Industrial and Applied Mathematics

    • Research Collaborator, Discrete Geometric Analysis for Materials Design, Grant-in-Aid for Scientific Research on Innovative Areas (Research in a proposed research area) (“B01-1 Searching for materials based on analysis of complex networks,” B01 Information Science for Materials Science)

    • Top 10 Books, Technical and Business Books Recommended for IT Specialists 2017, Shoeisha (Python Machine Learning)