Jingtun Zhang
Machine Learning Engineer at TikTok
Bellevue, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolJingtun Zhang is a machine learning engineer with eight years of experience focused on graph-structured data and self-supervised learning, currently contributing to R&D at TikTok in Seattle/Bellevue. He holds a MS in Computer Science from Texas A&M and began his research career as a research assistant there, building a strong foundation in academic and applied ML. On GitHub he has contributed to divelab/DIG, improving SSL methods and testing for graph neural networks—work that tightened model validation and feature-masking strategies in a library used by graph-ML researchers. Comfortable bridging research and production, he combines rigorous experimentation with practical engineering to move graph-ML ideas toward deployable systems. Colleagues describe him as methodical and detail-oriented, with a knack for improving testing and reproducibility in complex ML codebases.
8 years of coding experience
2 years of employment as a software developer
学士, Computer Science, 学士, Computer Science at 中国科学技术大学Master's degree, Computer Science, Master's degree, Computer Science at Texas A&M UniversityMaster's degree, Computer Science, Master's degree, Computer Science at 美国德克萨斯A&M大学
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (10)
pytorch10
deep-learning10
self-supervised-learning10
graph-neural-network10
testing10
python9
3d-graphics8
graph8
3d8
machine-learning7
Programming languages (5)
Github contributions (5)
divelab/DIG
Aug 2021 - Aug 2021
A library for graph deep learning research
Role in this project:
ML Engineer
ML EngineerContributions:8 commits, 8 pushes in 5 days
Contributions summary:Jingtun primarily contributed to the development and testing of self-supervised learning (SSL) methods for graph neural networks within the `dig` repository. Their commits focused on updating and expanding tests for various SSL models, including adjustments to existing methods and datasets. They also implemented and tested features related to the `Contrastive` framework, which involved modifying model paths and improving feature masking strategies. These changes improved the model and the testing of several graph deep learning methods.
explainable-mlpytorchdataminingdeep-learninggraph-deep-learning
OrdinaryCrazy/ParallelComputingAlgorithm
Apr 2019 - May 2019
Contributions:11 commits, 11 pushes, 1 branch in 1 month
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