We analyze scientific phenomena by combining mathematical modeling with deep learning, developing scientific machine learning models applicable across dynamical systems, industrial processes, medicine, and the life sciences.

Research areas

• Physics-Informed Neural Networks (PINNs)
• Parameter estimation for differential equations
• Density-PINN
• Inference of dynamical systems via machine learning
• Clinical data analysis and medical AI
• Learning from imperfect data
• Symbolic regression

Papers in progress

• Under reviewFrom Nonsmooth Minima to Smooth Branches via Heat Kernel Regularization
• Under reviewPrediction for Overall Survival in Multiple Myeloma with XGB-AFT
• Under reviewDeep Predictor-Corrector Networks for Robust Parameter Estimation in Non-autonomous System with Discontinuous Inputs

Grants & projects

• 2026.07 – 2031.06 AI + Science & Technology Research GrantOngoing National Research Foundation of Korea
• 2024.04 – 2029.03 NRF Excellent Young Researcher ProgramOngoing National Research Foundation of Korea
• 2021.03 – 2022.02 Blockchain platform development 국내 기업 · Industry
• 2020.11 – 2021.04 AI model for industrial intelligent controllers 국내 기업 · Industry
• 2020.11 – 2021.02 Thickness deviation model for cold rolling POSCO · Industry
• 2020.08 – 2020.10 Mobile device location tracking algorithm ELUON · Industry
• 2019.07 – 2020.02 Temperature prediction model for hot rolling POSCO · Industry
• 2017.07 – 2018.11 AI algorithms for steelmaking processes POSCO · Industry