Keunhyun Oh
소프트웨어 엔지니어 at 네이버클라우드(NAVER Cloud)
South Korea
Summary
👤
Senior🎓
Top SchoolKeunhyun Oh is a seasoned software engineer with 10 years' experience specializing in MLOps, data engineering, and HPC deployment across enterprise and startup environments in South Korea. He has led MLE teams and production ML deployment at companies like 수퍼톤 and NAVER Cloud, and worked on security and product engineering during a long tenure at AhnLab. Keunhyun’s open-source contributions include meaningful enhancements to Microsoft’s SynapseML LightGBM components—adding advanced sampling and SHAP/leaf prediction features and implementing a delegate pattern for adaptive learning rates. He combines hands-on model engineering with infrastructure know-how, shipping scalable serving and monitoring pipelines for real-world ML systems. A Yonsei University computer science master’s graduate, he brings both rigorous academic grounding and practical experience integrating ML workflows into enterprise CI/CD. Colleagues would note his knack for translating research-grade model improvements into production-ready, observable systems.
10 years of coding experience
학사, 컴퓨터, 학사, 컴퓨터 at 충남대학교석사, 컴퓨터과학, 석사, 컴퓨터과학 at Yonsei University 연세대학교
Japanese
Github Skills (10)
apache-spark10
machine-learning10
pyspark10
ai10
lightgbm10
scala10
data-science8
azure6
microsoft-azure6
python6
Programming languages (7)
Github contributions (5)
microsoft/SynapseML
Dec 2019 - Jan 2022
Simple and Distributed Machine Learning
Role in this project:
ML Engineer
ML EngineerContributions:15 commits, 17 PRs, 68 comments in 2 years 1 month
Contributions summary:Keunhyun primarily contributed to the LightGBM component of the `synapseml` repository, focusing on enhancements and new feature implementations within the LightGBM model. Their work included adding parameters like `posBaggingFraction`, `negBaggingFraction`, and `top_k` to various LightGBM training parameters and modifying relevant classes. They also added the featuresShapCol and leafPredictionCol for the LightGBMClassifierModel. Additionally, the user implemented the delegate design pattern to monitor training and update learning rates during training iterations.
fairness-mlpythoncaffe2model-deploymentcognitive-services
ocworld/AKService
Jun 2018 - Sep 2018
Contributions:41 releases, 66 commits, 51 pushes in 3 months
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