Seungyeup Lee
Software Engineer at 신한은행
Seoul, South Korea
Summary
🤩
Rockstar🎓
Top SchoolSeungyeup Lee is a Seoul-based software engineer with 8 years of experience focused on data platforms, large-scale data pipelines, and applied AI. Currently at Shinhan Bank, he brings practical production experience integrating ML-driven data workflows in enterprise settings, grounded in a Computer Science degree from Pusan National University. As an active open-source contributor and AI Engineer on GitHub, he improved anomalib’s dataset handling, CLI installation, and configuration API to make edge-friendly anomaly detection more robust and user-friendly. His background includes intensive training at Samsung’s SSAFY program, reflecting a strong combination of formal education, hands-on ML engineering, and platform-level systems thinking.
8 years of coding experience
Bachelor of Science - BS Computer Science and Engineering, Bachelor of Science - BS Computer Science and Engineering at Pusan National University
english (toeic speaking lv.7)
Stackoverflow
41reputation
3kreached
0answers
1question
Github Skills (15)
data-handling10
unsupervised-learning10
anomaly-detection10
python10
pytest9
testing9
pytorch9
computer-vision9
configuration-management8
data-science8
openvino7
openpyxl6
python-modules6
win32com6
excel6
Programming languages (3)
Github contributions (5)
open-edge-platform/anomalib
Apr 2023 - Nov 2024
An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.
Role in this project:
ML Engineer
ML EngineerContributions:15 reviews, 10 PRs, 2 pushes in 1 year 6 months
Contributions summary:Seungyeup primarily contributed to enhancing the anomaly detection library by adding functionality to process folder dataset paths as a list within the `anomalib` repository. Their changes involved modifying dataset loading procedures, updating existing code, and implementing a new test case to validate the list input functionality. Furthermore, they refactored installation procedures within the CLI and updated the configuration API to enable better integration with the model and data configurations. Their work demonstrates the addition of features that directly improved the library's data handling capabilities and user experience.
hyper-parameter-optimizationstate-of-the-arthyperdetection-libraryexperiment-management
harimkang/harimkang
Jul 2020 - Dec 2024
Contributions:27 pushes, 1 branch in 4 years 6 months
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