DeepONet Cardiovascular Modeling
Deep Operator Network for cardiovascular hemodynamics modeling with physics-informed constraints for patient-specific predictions.
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Professional Summary
My research lies at the intersection of biomedical engineering and AI,
developing non-invasive diagnostic systems using PPG signals.
I work closely with clinical partners at Severance Hospital on
IRB-approved studies for cardiovascular and emergency medicine applications.
Education
PhD Candidate, Mechanical Engineering
Yonsei University
BS, Mechanical Engineering
2016-03-01
2020-02-28
Pusan National University
Interests
Computational Fluid Dynamics in Cardiovascular Domain
Machine Learning for Cardiovascular Risk Prediction
PPG-based Hemodynamic Diagnostics
Lattice Boltzmann Method
Physics-Informed Neural Operators
Non-invasive Biomarker Prediction
Research Projects
PINO Coronary Flow Prediction
Physics-Informed Neural Operator (PINO) for efficient coronary artery blood flow simulation replacing traditional CFD methods.
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EASYCHECK Dehydration & Viscosity System
Wearable PPG-based system for real-time dehydration assessment and blood viscosity monitoring in clinical settings.
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PPG-based Fluid Loading Prediction
AI system for predicting fluid responsiveness using PPG spectrograms with ensemble deep learning models achieving AUC 0.85+
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FFR Prediction & Coronary Artery Diagnosis
Optimized FFR prediction algorithm for diagnostic gray zone using hemodynamic features, synthetic models, and biometric data beyond conventional vessel imaging approaches.
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Publications
(2025).
A Physics-Integrated Deep Learning Approach for Patient-Specific Non-Newtonian Blood Viscosity Assessment using PPG.
Computer Methods and Programs in Biomedicine, Vol. 265, 108740.
(2022).
Optimization of Tricuspid Membrane Mechanism for Effectiveness and Leaflet Longevity through Hemodynamic Analysis.
Engineering Applications of Computational Fluid Mechanics, 16(1), 1587-1600.
(2022).
Optimization of FFR Prediction Algorithm for Gray Zone by Hemodynamic Features with Synthetic Model and Biometric Data.
Computer Methods and Programs in Biomedicine, Vol. 220, 106827.
Awards & Honors
- Merit Academic Paper Award, Graduate School Innovation Outstanding Thesis Awards — Yonsei University, 2026
- Best Paper Award — BESCO (Biomedical Engineering Society for Circulation), 2022
- Merit Academic Paper Award — Yonsei University, 2022
- Best Paper Award — School of Mechanical Engineering, Yonsei University, 2022
Patents
H. J. Lee, J. S. Lee, “Non-Newtonian fluid viscosity modeling of patient blood using wearable device-based PPG and biometric information”
- Korea — Application No. 10-2024-0026709
H. J. Lee, J. S. Lee, “An algorithm that collects PPG and biometric information from wearable devices and analyzes it to predict systolic and diastolic viscosity”
- Korea — Application No. 10-2024-0028201
H. J. Lee, J. S. Lee, “Glucose and diabetes prediction algorithm using wearable device-based PPG and biometric information”
- Korea — Application No. 10-2023-0144347
J. S. Lee, W. R. Choi, H. J. Lee, “A wearable device for monitoring glaucoma suspect and a method for monitoring glaucoma suspect”
- Korea — Application No. 10-2023-0144347
H. J. Lee, J. S. Lee, “Glaucoma Diagnosis Method and System Based on Contactless Biosignals”
- Korea — Application No. 10-2023-0078883
H. J. Lee, J. S. Lee, S. C. Ko, “Noninvasive Urodynamics Test Method and Apparatus Based on Artificial Intelligence”
- Korea — Application No. 10-2022-0085583
J. S. Lee, Y. W. Kim, H. J. Lee, “Optimization system and method of AI algorithm for prediction coronary artery lesions based on FFR”
- Korea — Application No. 10-2022-0030019
- US — Application No. 17/820,819
Conferences
International Conferences
H. J. Lee, T. H. Han, J. S. Kim, S. G. Lee, J. S. Lee, “Patient-Specific Coronary Flow Field Prediction Using Physics-Informed Neural Operators”, Design of Medical Devices Conference (DMD2026), Minneapolis, MN, USA (2026) — accepted
H. J. Lee, J. H. Kim, Y. W. Kim, D. S. Kim, S. Y. Shin, J. H. Hong, J. S. Lee, “Patient-Specific Coronary Flow Field Prediction Using Physics-Informed Neural Operators”, The 18th Asian Congress of Fluid Mechanics (ACFM), Seoul, Korea (2025) — oral
H. J. Lee, Y. W. Kim, J. H. Kim, S. Y. Shin, S. L. Lee, C. H. Kim, J. S. Kim, K. S. Chung, J. S. Lee, “AI-based Hemorheology Prediction with Patient-Specific Biometric Boundary Conditions”, 2024 ICTAM, Daegu, Korea (2024) — short oral & poster
H. J. Lee, J. S. Lee, “Optimization of Artificial Intelligence Algorithms for FFR Prediction in Gray Zone”, 2022 ICTME, Gyeonggi-do, Korea (2022) — oral
H. J. Lee, Y. W. Kim, J. H. Kim, J. S. Lee, “Estimating CFD-based CT FFR using lattice Boltzmann method — 3D geometry auto segmentation and novel patient specific computation”, ESCHM-ISCH-ISB 2021 FUKUOKA, Fukuoka, Japan (2021) — oral
Domestic Conferences
H. J. Lee, J. S. Lee, “Unlocking Predictive Health Outcomes with Biometric Data”, KSME Conference 2023, Songdo, Korea (2023) — oral
H. J. Lee, J. S. Lee, “Modeling Coronary Artery Hemodynamics: Exploring DCNN Surrogate Models in Preliminary Research”, BESCO Summer Meeting, Daegu, Korea (2023) — oral
H. J. Lee, J. S. Lee, “Artificial Intelligence Algorithms for FFR Prediction in Gray Zone by Single-view Angiography”, BESCO Winter Meeting, Seoul, Korea (2022) — short oral & poster, Nominee for Best Paper Award
H. J. Lee, J. H. Kim, J. S. Lee, “Artificial intelligence-based automatic cardiovascular lesion prediction diagnostic device”, BESCO Summer Meeting, Jeju, Korea (2022) — poster
H. J. Lee, Y. W. Kim, J. S. Lee, “Optimization of Artificial Intelligence Algorithms for FFR Prediction in Gray Zone”, BESCO Winter Meeting, Gangwon, Korea (2021) — oral
Contact
Feel free to reach out via email or connect with me on the platforms below.
Email: kochujam369@gmail.com