Sang-wook Lee
Seoul, South Korea
Summary
👤
Senior🎓
Top SchoolSang-wook Lee is a research scientist with 11 years of experience applying robotics, computer vision, and multi-sensor fusion to real-world and space applications. He has developed image-based phenotyping pipelines using graph theory, integer programming, CNNs and RNNs at KIST, and previously designed robust localization, mapping, and moving-object detection frameworks for UAVs and asteroid scenarios at NASA Ames. His background spans academia and industry—from lecturing and mobile-robot research to leading robot offline-programming and simulation products—bringing both theoretical rigor (Ph.D. in Robotics) and practical systems delivery. Based in Seoul, he combines expertise in hyperspectral/NIR/thermal imaging and Bayesian multi-sensor fusion, with a recurring focus on making autonomous systems reliable in challenging environments. An avowed “robot advocate,” he uniquely bridges plant phenotyping and space-grade autonomy, seeking to put robots on our side across scales from crops to cosmos.
11 years of coding experience
2 years of employment as a software developer
Master’s Degree, Mechanical Engineering, 3.70 / 4.0, Master’s Degree, Mechanical Engineering, 3.70 / 4.0 at Yonsei UniversityDoctor of Philosophy (Ph.D.), Robotics, 3.43 / 4.3, Doctor of Philosophy (Ph.D.), Robotics, 3.43 / 4.3 at Korea Advanced Institute of Science and Technology
Korean, English
Github Skills (27)
pattern-recognition10
artificial-intelligence10
robotics10
testing10
machine-learning10
reinforcement-learning10
deep-learning10
computer-vision10
parser7
korean-nlp6
pdf6
language-model6
neural-machine-translation6
morphology5
keras5
Programming languages (4)
Github contributions (5)
sangwook236/SWAF
May 2015 - Jul 2020
Sang-Wook's Analysis Framework (SWAF)
Contributions:37 commits, 1 PR, 35 pushes in 5 years 2 months
analysis-frameworkswaf
sangwook236/SWDT
Aug 2015 - Jan 2023
Sang-Wook's Development and Testing (SWDT)
Contributions:1548 commits, 21 PRs, 966 pushes in 7 years 6 months
testingartificial-intelligencecomputer-visiondeep-learningfailure-analysis
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