Hwalsuk Lee
Chief Technology Officer at Upstage
South Korea
Summary
🤩
Rockstar🎓
Top SchoolHwalsuk Lee is a CTO and AI leader with nine years of focused experience shepherding applied research into production-grade products, currently leading Upstage's mission to make AI beneficial. He combines deep academic training from KAIST with hands-on research roles at NAVER, NCSOFT, and Samsung, where he worked on computer vision, deep RL, GANs, and video analytics. As an active member of the TensorFlow Korea community, he blends open collaboration with product delivery and has contributed practical generative-model implementations and visualization tools to the TensorFlow ecosystem. His background shows a pattern of turning advanced generative and vision research into deployable systems for real-world use cases. Colleagues describe him as a technically rigorous leader who still dives into code and model debugging, improving maintainability and interpretability. Based in South Korea, he uniquely balances academic depth with startup-scale execution and community stewardship.
9 years of coding experience
4 years of employment as a software developer
학사, Electrical and Electronics Engineering, 학사, Electrical and Electronics Engineering at 아주대학교박사, Electrical and Electronics Engineering, 박사, Electrical and Electronics Engineering at 한국과학기술원(KAIST)
Github Skills (12)
tensorflow10
generative-adversarial-network10
deep-learning8
cgan8
machine-learning8
model-building8
model-driven8
dcgan8
modeling8
model-driven-development8
python8
mnist6
Programming languages (3)
Github contributions (5)
hwalsuklee/tensorflow-generative-model-collections
Aug 2017 - Mar 2018
Collection of generative models in Tensorflow
Role in this project:
ML Engineer
ML EngineerContributions:70 commits, 6 PRs, 68 pushes in 7 months
Contributions summary:Hwalsuk primarily contributed to the development of various generative models using TensorFlow. Their work involved adding source code for ACGAN, CVAE, VAE, WGAN-GP, and correcting bugs in existing implementations such as CGAN and infoGAN. They also addressed minor bugs, corrected typos, and refactored code for better maintainability. Moreover, the user incorporated visualization functions for model analysis, showcasing a focus on both model development and evaluation.
tensorflowvaemnistautoencoderacgan
hwalsuklee/tensorflow-fast-style-transfer
Feb 2017 - Mar 2018
Contributions:46 commits, 45 pushes, 1 branch in 1 year 1 month
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