Seungwoo Han

Biosignal Informatics Lab, Tokyo University of Agriculture and Technology (TUAT), Japan

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Seungwoo Han (Student Member of IEEE and ACM) is currently pursuing the Ph.D. degree in electrical engineering and computer science at TUAT, Japan.

He worked as a backend developer in industry from 2020 to 2024. His primary research interests lie in resource-efficient AI inference, with topics including but not limited to:


- Wrapper-based feature selection
- Spiking neural networks
- Hardware acceleration using high-level synthesis

selected publications

  1. GCCE
    External Sinkhole Attack Detection in Large-Scale WSNs Using Metaheuristic Feature Selection (Accepted)
    Seungwoo Han, Sawako Kitagata, Ingon Chanpornpakdi, and 2 more authors
    In 2026 IEEE Global Conference on Consumer Electronics (GCCE), 2026
  2. SoftwareX
    han2026pywsnsim.png
    PyWSNSim: A Python-based component-oriented simulation framework for sinkhole attack analysis in large-scale WSNs
    Seungwoo Han, Toshihisa Tanaka, and Su Man Nam
    SoftwareX, 2026 (JCR 2025 Impact Factor: 1.9, Q3)
    SCIE DOI Code
    0 Google Scholar citations
  3. ITC-CSCC
    han2025lightweight.png
    A lightweight multi-feature fusion deep learning architecture for human ECG reconstruction from chest-worn accelerometer
    Seungwoo Han, Ingon Chanpornpakdi, Puwadej Leelasiri, and 4 more authors
    In 2025 International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC), 2025
    Best Paper Awards DOI
    0 Google Scholar citations