# Hyeong Jun Lee (이형준)

> Postdoctoral Researcher at the Multiscale Fluid Dynamics Lab, School of Mechanical Engineering, Yonsei University (Seoul). I turn clinical questions into engineering problems and bring back answers clinicians can act on, using simulation, AI and wearable signals.

- Role: Postdoctoral Researcher, Multiscale Fluid Dynamics Lab, School of Mechanical Engineering, Yonsei University; concurrently Project Researcher, Yonsei University Health System, 4D CT-FFR project (PI: Prof. Jung-Sun Kim, Department of Internal Medicine, College of Medicine).
- Ph.D. in Mechanical Engineering, Yonsei University, completed Aug. 2026.
- Fields: computational hemodynamics, physics-informed AI, wearable biosignals and medical devices.
  - Simulation: Patient-specific CFD from CT, read out as FFR and wall shear stress; also a tricuspid valve device.
  - Physics-informed AI: Neural operators give a vessel's pressure field in 1.78 s instead of 35 minutes; synthesized 4D CT gives FFR across the cycle.
  - Wearable biosignals: 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.
  - Devices and translation: FFR simulator, tricuspid implant, negative-pressure tunnel, AI urodynamics; 16 patents, 2 registered; parts ship in CARDIOS and EASYCHECK.
- Research projects (/research), newest first:
  - 4D flow risk assessment for anomalous coronary origin in children (2025–2028)
  - Neural operator for patient-specific coronary flow (2024 –)
  - Dynamic FFR from diffusion-synthesized 4D CT (2024–2026)
  - Fluid-bolus prediction from smartwatch PPG (2024 –)
  - Patient-specific coronary artery disease risk analysis software (2024–2026)
  - Blood viscosity from smartwatch PPG (2023–2025)
  - Wearable screening for glucose, diabetes and glaucoma (2023–2024)
  - Real-time precision medicine platform on smart hemodynamic indices (2022–2029)
  - Variable negative-pressure tunnel for respiratory infection control (2022–2024)
  - AI-based non-invasive urodynamics test (2022)
  - FFR prediction in the gray zone (2020–2022)
  - CFD-based CT-FFR with the lattice Boltzmann method (2020–2022)
  - High-speed point-of-care FFR simulator (2020–2022)
  - Tricuspid regurgitation membrane device (2020–2025)
- Recent:
  - Sep 2026: Started as a postdoctoral researcher at Yonsei University.
  - Aug 2026: Completed the Ph.D. in Mechanical Engineering at Yonsei University.
  - 2026: A co-authored paper appeared in CMPB.
  - 2026: A first-author paper appeared in DMD.
  - 2026: A co-authored paper appeared in DMD.
  - 2026: Received the Merit Academic Paper Award from the School of Mechanical Engineering, Yonsei University.
  - Dec 2025: The patent “AI-based noninvasive urodynamics test method and apparatus” was registered.
  - Nov 2025: The patent “Optimization system and method of AI algorithm for prediction coronary artery lesions based on FFR” was registered.
- In brief:
  - First author · DMD 2026 · CMPB 2025 · CMPB 2022 (co-first) · Merit Academic Paper Award 2026
  - PINO · 85 patients, 10-fold cross-validation · CFD up to 35 min on 64 CPU cores · 1.78 s on one GPU
  - 16 patents (2 registered) · 7 funded projects (participating researcher) · 4 awards
- Record: 4 journal articles · 2 proceedings · 16 talks · 16 patents (2 registered) · 7 funded projects (participating researcher) · 4 awards.
- Contact: hjunlee96@gmail.com. Open to collaboration, positions and paper requests.

## Links

- [Email](mailto:hjunlee96@gmail.com)
- [CV (PDF)](https://hyeongjunlee.me/uploads/resume.pdf)
- [LinkedIn](https://www.linkedin.com/in/hyeongjunlee/)
- [Google Scholar](https://scholar.google.com/citations?user=1NxM3T0AAAAJ&hl=en)
- [ORCID](https://orcid.org/0009-0001-5691-3787)

## Pages

- [Home](https://hyeongjunlee.me/): Who I am, the live CFD | PINO vessel plate, four rows on what I work on, and three selected papers. Markdown twin: [https://hyeongjunlee.me/index.md](https://hyeongjunlee.me/index.md)
- [Research](https://hyeongjunlee.me/research/): Every research project, newest first, each with one sentence and its papers, patents, funded projects and talks; three carry a live demo with its method and a metrics table. Markdown twin: [https://hyeongjunlee.me/research.md](https://hyeongjunlee.me/research.md)
- [CV](https://hyeongjunlee.me/cv/): The whole record: education, appointments, every publication, talks, patents, funded projects, awards, teaching and skills. Markdown twin: [https://hyeongjunlee.me/cv.md](https://hyeongjunlee.me/cv.md)

## Machine-readable

- [llms-full.txt](https://hyeongjunlee.me/llms-full.txt): the full CV and every research project with its outputs (the three demos with their metrics), in one markdown file.
- [cv.json](https://hyeongjunlee.me/cv.json): the CV in JSON Resume format.
- [CV (PDF)](https://hyeongjunlee.me/uploads/resume.pdf): the printable CV.

## Notes for agents

- PINO metrics are the current model's (current model, 85 cases, 10-fold CV), not the DMD 2026 proceedings' numbers. The demo case is well predicted (pressure R² rank 13 of 85); cohort medians are given beside it.
- Fluid-bolus numbers come from M1, manuscript under revision (EAAI).
- Author roles are stated per entry (first, co-first, co-author); please keep them when citing.

Updated 2026-10-21