Rui Zhu
Staff Data Scientist
Raleigh, North Carolina, United States
Summary
🤩
Rockstar🎓
Top SchoolRui Zhu is a Staff Data Scientist with eight years of experience applying advanced statistical modeling and machine learning to ads measurement and real-world problems, currently leading advertiser guidance at Meta after senior data-science roles at Google. He holds a Ph.D. in Statistics from NC State with research in semi-supervised learning, Bayesian nonparametrics, and ROC analysis, and has published in top applied statistics journals. Rui blends rigorous academic foundations with production impact—designing causal attribution models and end-to-end pipelines that improved Google Ads performance and revenue. He is also an active ML infrastructure contributor, having improved CI/CD and optimizer passes in prominent open-source projects such as ONNX and PyTorch/Glow, reflecting strengths in automation and model interoperability. Proficient across R, Python, SQL, Spark, and SAS, he pairs large-scale engineering chops with deep statistical intuition to ship robust, reproducible solutions.
8 years of coding experience
5 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Statistics, Doctor of Philosophy (Ph.D.) Statistics at North Carolina State UniversityBachelor of Applied Science (B.A.Sc.) Mathematics and Statistics, Bachelor of Applied Science (B.A.Sc.) Mathematics and Statistics at Zhejiang University
English, Chinese, Chinese
Github Skills (19)
python10
bash10
cmake10
onnx10
circleci10
script10
ci-cd-pipeline10
build-automation10
c-language9
neural-network9
deep-learning9
cprogramming-language9
deep-neural-networks8
machine-learning8
llvm8
Programming languages (7)
Github contributions (5)
onnx/onnx
Jun 2018 - Feb 2019
Open standard for machine learning interoperability
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:32 commits, 65 PRs, 19 pushes in 8 months
Contributions summary:Rui primarily contributed to the optimization and extension of the ONNX library. Their work involved implementing a new optimizer pass to extract constants to initializers, modifying existing test cases, and adding a test case related to ConvTranspose output shape. Additionally, they integrated retry logic for model downloading and addressed bugs to enhance the robustness of model retrieval. The user's contributions focused on improving the functionality and reliability of the library, indicating their involvement in back-end development and machine learning model optimization.
pytorchmxnetdeep-learninginteroperabilitymachine-learning
pytorch/glow
Oct 2018 - Feb 2020
Compiler for Neural Network hardware accelerators
Role in this project:DevOps Engineer & Automation Engineer
DevOps Engineer & Automation EngineerContributions:47 reviews, 35 commits, 111 PRs in 1 year 4 months
Contributions summary:Rui primarily focused on migrating the project's continuous integration and continuous deployment (CI/CD) processes to CircleCI, demonstrating a focus on automation and build management. This involved configuring build environments, including setting up dependencies and compilers, and integrating tests. The user also worked on setting up sccache wrappers and configuring build types for debug and release builds, improving the development workflow. The changes also included adapting the build process for different CI/CD environments like "PYTORCH".
hardware-acceleratorscompilerneural-networkacceleratorshardware
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