About

Hi! I’m a Master’s student in Industrial and Management Engineering at Hanyang University ERICA, advised by Prof. Stephane Barde at the Industrial Operations & Data Analytics (IODA) Lab.

My research focuses on Prognostics and Health Management (PHM) for lithium-ion batteries, with emphasis on deploying deep learning models on resource-constrained edge devices through model compression techniques. I am also actively researching in-situ defect detection and component fatigue prediction to enhance industrial reliability.

Research Interests

• Prognostics and Health Management (PHM)
• Time-Series Forecasting
• Physics-Informed Neural Networks
• Additive Manufacturing

News

• 2025.12 🏆 Minister of Science and ICT Award – Excellence Scholarship for Master’s Students in Science and Engineering
• 2025.12 🏆 Grand Prize at Factory Hack Korea 2025 (Ministry of Trade, Industry and Resources)
• 2025.11 📄 Paper published in Journal of Energy Storage (Co-Author)
• 2025.10 📄 Paper published in Energy (First Author)
• 2025.10 🏆 Best Paper Award at IEEE Global Reliability & PHM Conference
• 2025.10 📝 Patent filed for integrated compression pipeline for battery prognostics
• 2025.06 🏆 Excellence in Presentation Award at The Korean Reliability Society (KORAS) Spring Conference

Selected Publications

• Y. Seo, T. Kim, S. Barde, “Enhancing Battery SOH Prediction with Physics-Informed Neural Networks in Data-Scarce Environments,” Energy, 2025.

• T. Kim, Y. Seo, S. Barde, “Edge-compatible SOH Estimation for Li-ion Batteries via Hybrid Knowledge Distillation and Model Compression,” Journal of Energy Storage, 2025.

See Publications for the complete list.

Education

• M.S. in Industrial and Management Engineering, Hanyang University (2025 - Present)
• B.S. in Applied Mathematics, Hanyang University ERICA (2020 - 2025)
• B.S. in Computer Science (Double Major), Hanyang University ERICA (2021 - 2025)

Experience

• Intern, CMES AI ROBOTICS, Mar - Jun 2024
  • Developed a Human Action Recognition (HAR) and detection model for safety management; demonstrated the system at KOREA MAT 2024.
  • Conducted research on model architectures to enhance performance in Small Object Detection
  • Participated in (or Led) regular seminars reviewing latest Computer Vision literature.