Zeyang Ye
Advanced Analytics Research Scientist at Rockwell Automation
Irvine, California, United States
Summary
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Rockstar🎓
Top SchoolZeyang Ye is an Advanced Analytics Research Scientist with nine years of engineering experience, currently applying ML and AI research to industrial analytics at Rockwell Automation. He holds a master's in computer engineering from UC Irvine and began his academic journey with a computer science degree from Tsinghua University, now also affiliated with THU CS as a Ph.D. student contributor on graph ML tooling. Zeyang has built production-ready GPT-powered conversational systems integrating real-time iTRAK data and embedding-based retrieval, and contributed core fixes and model support to the notable AutoGL graph AutoML project. His background spans industry internships (including Sogou) and academic research, blending applied systems delivery with machine reading comprehension and graph learning expertise. Colleagues praise his ability to lead end-to-end projects— from data collection and knowledge-base construction to robust testing and scalable deployment—while surfacing subtle data-type and subgraph issues that improve model reliability.
9 years of coding experience
1 year of employment as a software developer
Bachelor's degree Computer Science, Bachelor's degree Computer Science at Tsinghua UniversityUniversity of California, Irvine
Github Skills (8)
pytorch10
machine-learning10
deep-learning10
graph-neural-network10
automl10
python9
neural-architecture-search8
testing7
Programming languages (4)
Github contributions (5)
THUMNLab/AutoGL
Feb 2021 - Dec 2022
An autoML framework & toolkit for machine learning on graphs.
Role in this project:
ML Engineer
ML EngineerContributions:69 commits, 21 PRs, 38 pushes in 1 year 10 months
Contributions summary:Zeyang's commits primarily focus on modifying and extending the AutoGL framework for machine learning on graphs. Their contributions include fixing subgraph-related issues, addressing dtype inconsistencies within feature engineering pipelines, and enhancing feature engineering tests for node classification. Additionally, the user added new tests and support for various machine learning models such as Enas and pure rl algorithms and related components.
pytorchdata-sciencedeep-learningneural-architecture-searchhyper-parameter-optimization
wondergo2017/DIDA
Oct 2022 - Dec 2022
Contributions:5 commits, 3 pushes, 1 branch in 1 month
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