Jeongmin Lee
Senior Research Scientist at Instagram
San Francisco Bay Area United States
Summary
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Senior🎓
Top SchoolJeongmin Lee is a Senior Research Scientist based in the San Francisco Bay Area with a decade of experience building internet-scale personalization and ranking systems at Meta and Instagram. He specializes in sequence models and user history modeling for feeds and Reels, translating cutting-edge research into production recommender systems. His work spans research, engineering and open-source contributions—he was a core developer and notebook contributor to NVIDIA's widely used Transformers4Rec library, demonstrating PySpark/PetaStorm data pipelines and transformer-based training techniques. Jeongmin holds a PhD in Computer Science from the University of Pittsburgh and brings a history of applied ML across industry internships and impactful volunteer software projects in global health.
9 years of coding experience
3 years of employment as a software developer
Exchange Program Computer Science, Exchange Program Computer Science at Rensselaer Polytechnic InstitutePh.D. in Computer Science, Ph.D. in Computer Science at University of PittsburghBachelor of Science Computer Science Global Entrepreneurship, Bachelor of Science Computer Science Global Entrepreneurship at Handong Global University
Github Skills (12)
data-preprocessing10
huggingface-transformers10
transformer10
machine-learning10
pytorch10
pyspark10
recommender-system10
tabular9
datatable9
data-engineering9
nlp9
bert8
Programming languages (6)
Github contributions (5)
NVIDIA-Merlin/Transformers4Rec
Jun 2020 - Oct 2020
Transformers4Rec is a flexible and efficient library for sequential and session-based recommendation and works with PyTorch.
Role in this project:
Data Scientist & ML Engineer
Data Scientist & ML EngineerContributions:146 commits in 3 months
Contributions summary:Jeongmin contributed a notebook demonstrating PySpark-based data preprocessing, data loading with PetaStorm, and the implementation of a Hugging Face transformer model. The work included loading and processing e-commerce data, converting timestamps, and performing feature engineering. They also updated a runner file for the transformer model and implemented a negative sampling approach for model training.
xlnetbertlanguage-modelseq2seqrecommendation
leej35/leej35.github.io
Jun 2016 - Aug 2024
Contributions:105 pushes in 8 years 4 months
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