
PulsePath: Two-Stage Generative Waveform Refinement for Robust Remote Photoplethysmography Estimation
A two-stage framework combining flow matching and residual diffusion to refine robust rPPG waveforms.
UNDER REVIEWM.S. Student · Jeonbuk National University
Physiological Computing & Computer Vision Researcher
Electronic & Information Engineering · advised by Prof. Sang Jun Lee
How reliably can ordinary cameras recover physiological signals outside controlled settings?
I work on robust remote PPG, non-contact stress estimation, egocentric vision, and medical image analysis.
01 / RESEARCH
My research combines computer vision, biosignal processing, and deep learning to measure physiological signals without physical contact.
Current projects cover robust remote photoplethysmography (rPPG), stress estimation, and heart-rate estimation from egocentric NIR eye video. My earlier work includes maxillary-sinus segmentation in panoramic X-rays and knowledge distillation for semantic segmentation.
Long term, I am interested in physiological sensing with egocentric and wearable cameras.
02 / SELECTED WORK
Selected journal and conference work in physiological sensing, medical imaging, and computer vision.
Showing all 8 works.

A two-stage framework combining flow matching and residual diffusion to refine robust rPPG waveforms.
UNDER REVIEW
Temporal learning for contact-free heart-rate sensing in the challenging egocentric near-infrared eye setting.
ECCV WORKSHOP
Pulse-rate-variability guidance meets selective state-space modeling for camera-based stress recognition.

A semi-supervised framework that uses weighted knowledge distillation to suppress unreliable signals from teacher–student structural mismatches and SinusCycle-GAN to refine pseudo-labels.

An attention-enhanced U-Net transformer for delineating maxillary sinus regions in dental panoramic radiographs.

A lightweight student network learns channel-aware feature importance for outdoor driving-scene segmentation.
Jeju Gravel Hotel · Nov 13–16, 2024
Jeonbuk National University International Convention Center · Jun 25–27, 2025
03 / JOURNEY
Building a foundation across electronics, computer vision, and physiological computing.
CERTIFICATION · 2023Microsoft · Credential ID: GTcD-4wBm
View credential ↗MAR 2026 — PRESENT
Jeonbuk National University · Jeonju, Republic of Korea
Advised by Prof. Sang Jun Lee. Researching robust rPPG and contact-free stress estimation.
MAR 2022 — FEB 2026
Jeonbuk National University · Jeonju, Republic of Korea
Developed a foundation in signal processing, embedded systems, computer vision, and deep learning.
04 / RECOGNITION
10 awards and honors in research, AI, engineering design, entrepreneurship, internships, and mentoring (2022–2025).
Representative achievements
National Contest of Champions
Seoul National UniversityStartup Idea Challenge for Social Big Data Analysis
University of SeoulCreative Engineering Design Contest
Jeonbuk National UniversityShared across awards
Keywords used by 2+ awardsFour keywords are shared by two or more awards. Select one to highlight every match.
Complete record
05 / OPEN TO OPPORTUNITIES
I work on camera-based physiological sensing, computer vision, and medical AI. I’m exploring research and AI engineering opportunities, and I’m also happy to discuss collaborations.