Sejong Yang
AI Scientist at LG AI Research
Seoul, South Korea
Summary
👤
Senior🎓
Top SchoolSejong Yang is an AI Scientist with a decade of experience combining research-grade machine learning with production software engineering, currently developing domain-aware foundation vision-language models at LG AI Research in Seoul. He holds a PhD-track background from Yonsei University and has interned at Adobe and NAVER, working on generative models for talking characters and practical multimodal filtering systems. Sejong contributes to open-source ML tooling—having fixed bugs and refactored backend code in the well-known TensorLy project—highlighting his attention to numerical correctness and testable code. His career spans data engineering for Video LLMs, cross-platform app development, and privacy-aware backend services, showing an uncommon blend of low-level engineering and cutting-edge AI research.
10 years of coding experience
1 year of employment as a software developer
Doctor of Philosophy - PhD, 컴퓨터 과학, Doctor of Philosophy - PhD, 컴퓨터 과학 at Yonsei UniversityExchange Student, Department of Informaion Technology, Exchange Student, Department of Informaion Technology at Uppsala University
Korean, English
Stackoverflow
149reputation
8kreached
16answers
3questions
Github Skills (18)
back-end-development10
python10
algebra10
decomposition10
tensorrt10
numpy10
tensor10
backend10
scipy9
driver6
asp-net6
php6
git-svn6
docker6
linux6
Programming languages (12)
Github contributions (5)
tensorly/tensorly
Aug 2019 - Dec 2019
TensorLy: Tensor Learning in Python.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:6 commits, 4 PRs, 4 comments in 4 months
Contributions summary:Sejong primarily focused on bug fixes and code refactoring within the `tensorly` backend. Their contributions included resolving a TypeError related to the `partial_svd` function, ensuring proper handling of `None` values for `n_eigenvecs`. They also addressed code style by using UTF-8 encoding and integrated a merge of code from a different user branch, and performed auto-formatting. Additionally, they refactored code to pass tests within the project.
tensor-factorizationtensor-decompositionpythontensor-learningtensor
kingsj0405/dev-env
Mar 2018 - Apr 2019
Contributions:10 commits, 40 pushes, 3 branches in 1 year 1 month
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