Yuan Yuan
Member Of Technical Staff at OpenAI
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolYuan Yuan is a Member of Technical Staff at OpenAI and an assistant professor at UC Davis with 12 years of experience bridging machine learning research and causal inference. He develops computational techniques that combine ML and causal methods for online field experiments (A/B testing) and uses large-scale data to study social and organizational networks. Trained at MIT (PhD) and Tsinghua (BS in CS and Economics), he has held roles at Purdue, the MIT Media Lab, Microsoft, and Meta, moving fluidly between academia and industry. Beyond academic work, he contributes to core ML infrastructure—e.g., meaningful ONNX changes to onnx.proto and LSTM/type-shape handling—highlighting a rare ability to translate rigorous causal methods into production-ready tooling. Based in San Francisco, he’s known for turning principled statistical ideas into scalable experimental and network-analysis systems.
13 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy - PhD, Social Engineering Systems & Statistics, Doctor of Philosophy - PhD, Social Engineering Systems & Statistics at Massachusetts Institute of TechnologyBachelor's degree, Computer Science and Economics, Bachelor's degree, Computer Science and Economics at Tsinghua University
Github Skills (14)
protobuff10
machine-learning10
deep-learning10
onnx10
protobuf310
protobufs10
protobuf10
python9
pytorch8
tensorflow28
tensorflow8
deep-neural-networks7
ml7
neural-network7
Programming languages (4)
Github contributions (5)
onnx/onnx
Sep 2017 - Jan 2018
Open standard for machine learning interoperability
Role in this project:
ML Engineer
ML EngineerContributions:52 commits, 16 PRs, 59 pushes in 3 months
Contributions summary:Yuan's commits primarily focused on modifications to the `onnx.proto` file and related Python helper functions. The changes involve restructuring data types, shapes, and value information within the graph representation used by ONNX. The contributions included updates to the LSTM operator and overall adjustments aimed at improving the representation and handling of types and shapes in ONNX models.
pytorchmxnetdeep-learninginteroperabilitymachine-learning
yuanbyu/XobotOS
Jan 2013 - Jan 2013
Contributions:7 commits in 9 days
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