Faculty Introduction Ja

SHIMAUCHI, Hirokazu

Associate Professor
Research Fields
Machine Learning
Academic Background
Yamanashi Eiwa College, Tokyo Institute of Technology, Tokyo Foundation for Policy Research, Hachinohe Institute of Technology
Subjects in Charge (Undergraduate)
Machine Learning 1, Machine Learning 2, Basics of Data Science, Applied Data Science, Operating Systems, Mathematics Practice 2, Systems Information Science Practice, Introduction to Modeling
Subjects in Charge (Graduate School)
Advanced Topics in Mathematical Analysis
Degree
Ph.D. in Information Sciences, Tohoku University

Message for Students

Let us actively engage in various activities while acquiring knowledge and skills on our own.

Research Contents

I am conducting research on constructing and applying machine learning methods to uncover hidden patterns in data. Specifically, I am working on representation learning techniques that derive useful features for prediction from data. I am also developing methods to detect outliers that significantly differ from overall data trends. Additionally, I collaborate with researchers from various disciplines on applying machine learning to issues in the social sciences and other fields.

Attractive Factors of My Research

I am captivated by the point to freely constructing algorithms related to the mechanisms by which machines learn automatically, using new ideas and approaches. I aim to conduct research that transforms zero into one, without being confined by existing frameworks.

Achievement

  • Best Paper Candidates, IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT 2019), 2019.
  • Best Paper Candidates, 15th International Joint Conference on Computational Intelligence (IJCCI 2023): Neural Computation Theory and Applications (NCTA 2023), 2023.

Major Books and Papers

  • H. Shimauchi, Unsupervised Representation Learning by Quasiconformal Extension, In Proceedings of the 15th International Joint Conference on Computational Intelligence, 1, 440-449, 2023.
  • H. Shimauchi, An Activation Function with Probabilistic Beltrami Coefficient for Deep Learning, In Proceedings of the 14th International Conference on Agents and Artificial Intelligence, 3, 613-620, 2022.
  • H. Shimauchi, Improving Supervised Outlier Detection by Unsupervised Representation Learning and Generative Adversarial Networks, In Proceedings of the 4th International Conference on Information Science and Systems, 22-27, 2021.
  • S. Kato, T. Nakanishi, B. Ahsan, H. Shimauchi, Time-series topic analysis using singular spectrum transformation for detecting political business cycles, Journal of Cloud Computing, 10, 21, 1-16, 2021.
  • S. Kato, T. Nakanishi, H. Shimauchi, B. Ahsan, Topic Variation Detection Method for Detecting Political Business Cycles, In Proceedings of the 6th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, 85-93, 2019.