Hyeong Jun Lee
이형준Postdoctoral Researcher · Multiscale Fluid Dynamics Lab, Yonsei University Postdoctoral Researcher · Yonsei University computational hemodynamics · physics-informed AI · wearable biosignals · medical devices
I turn clinical questions into engineering problems and bring back answers clinicians can act on, using simulation, AI and wearable signals.
Sep 2026Started as a postdoctoral researcher at Yonsei University.
- Aug 2026Completed the Ph.D. in Mechanical Engineering at Yonsei University.
- 2026A co-authored paper appeared in CMPB.
- 2026A first-author paper appeared in DMD.
- 2026A co-authored paper appeared in DMD.
- 2026Received the Merit Academic Paper Award from the School of Mechanical Engineering, Yonsei University.
- Dec 2025The patent “AI-based noninvasive urodynamics test method and apparatus” was registered.
- Nov 2025The patent “Optimization system and method of AI algorithm for prediction coronary artery lesions based on FFR” was registered.
CFD: FFR 0.83, up to 35 minutes on 64 CPU cores. PINO: FFR 0.82, 1.78 seconds on one GPU.
Work
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Patient-specific CFD from CT, read out as FFR and wall shear stress; also a tricuspid valve device.
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Neural operators give a vessel's pressure field in 1.78 s instead of 35 minutes; synthesized 4D CT gives FFR across the cycle.
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Smartwatch PPG gives blood viscosity and predicts a fluid bolus within 24 h (AUC 0.895, under revision); patents cover blood pressure, glucose and glaucoma.
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FFR simulator, tricuspid implant, negative-pressure tunnel, AI urodynamics; 16 patents, 2 registered; parts ship in CARDIOS and EASYCHECK.
Selected
- 2026
Patient-Specific Coronary Flow Field Prediction Using Physics-Informed Neural Operators
- 2025
A Physics-Integrated Deep Learning Approach for Patient-Specific Non-Newtonian Blood Viscosity Assessment using PPG
- 2022
Optimization of FFR prediction algorithm for gray zone by hemodynamic features with synthetic model and biometric data