Minje Park
Seoul, Korea, Republic of
Summary
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RockstarMinje Park is a machine learning researcher based in Seoul with 3+ years of hands-on experience developing and deploying AI models for clinical applications, particularly ECG-based diagnostics and monitoring. At VUNO she led renal disease detection to medical-device approval, built lightweight on-device ECG models, and is driving ECG foundation-model research with self-supervised and semi-supervised methods for time-series segmentation and multi-modal (ECG-text) analysis. Her background from KAIST and Sungkyunkwan University combines systems engineering rigor with practical product delivery, including successful mobile and kiosk deployments. She focuses on label-efficient training and cross-device adaptation, translating advanced research into robust, resource-efficient systems for real-world healthcare.
3 years of coding experience
Github Skills (19)
incremental-learning10
semi-supervised-learning10
image-segmentation10
self-supervised-learning10
raw-image10
transfer-learning10
image-classification9
pytorch9
openvino9
machine-learning9
xpu9
deep-learning9
automl9
nvidia9
quantization9
Programming languages (1)
Github contributions (2)
minjepar/ddpm_pytorch
Dec 2022 - Jan 2023
Contributions:15 pushes, 1 branch, 1 comment in 29 days
open-edge-platform/geti
Oct 2022 - Oct 2022
Build, train, optimize, and run computer vision models locally, from raw images to live inference. Open source, with CPU, Intel XPU, and NVIDIA CUDA support.
Contributions:5 commits, 4 pushes in 8 days
computer-visioncudainferencenvidiaraw-image
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