List of faculty members

SHIMAUCHI, Hirokazu Associate Professor

Research Contents

My research focuses on developing and applying machine learning methods that uncover underlying patterns in data. In particular, I seek to capture the geometric structures inherent in data and to develop efficient learning methods that remain effective under limited computational resources. My recent work includes representation learning and anomaly detection based on quasiconformal mapping theory, novel activation and representation transformation methods for deep learning, and feature representation learning through noncommutative quantum dynamics implemented on quantum computers. I also collaborate with researchers across disciplines to apply machine learning to a wide range of fields, including the social sciences and fluid dynamics.

Attractive Factors of My Research

What I find most compelling about this field is the freedom to explore new ideas and approaches for designing algorithms that enable machines to learn from data. Rather than remaining within established frameworks, I aim to pursue research that creates genuinely new possibilities—work that turns “zero into one.”

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.
• Best Paper Candidates, 6th International Conference on Image Processing and Vision Engineering (IMPROVE 2026), 2026

Major Books and Papers

• H. Shimauchi, Noncommutative Quantum Dynamics for Feature Representation Learning, IEEE Access, 14, 53529-53571, 2026.
• H. Shimauchi, Geometry-Aware Stochastic Structured Channel Mixing for Deep Neural Networks, Accepted for publication in the Proceedings of 6th International Conference on Image Processing and Vision Engineering, Communications in Computer and Information Science, Springer, 2026.
• H. Shimauchi, Quasiconformal Extension-Based Unsupervised Representation Learning and Application to Semi-supervised Outlier detection, Computational Intelligence, 187–210, 2025.
• 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.
  
  (Joint research with undergraduate and graduate students in my laboratory; presentations in English only)
• S. Kon, H. Shimauchi, Lightweight Deep Learning for Anomaly Detection on Edge Devices via Depthwise Separable Convolution and Knowledge Distillation, Accepted at in the Proceedings of IEEE GCCE 2026, 2026.
• Y. Kanda, H. Shimauchi, Y. Katori, Knowledge Distillation From TSMixer to Echo StateNetwork for Multivariate Time-Series Forecasting, IEICE GlobalNet Workshop 2026, 2026.