Yuan Yuan
Member Of Technical Staff at OpenAI
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolYuan Yuan is a researcher-engineer bridging machine learning, causal inference, and network analysis with 13 years of experience across academia and industry. Currently a Member of Technical Staff at OpenAI and an assistant professor at UC Davis (formerly at Purdue), she focuses on computational methods for A/B testing, causal estimation, and the study of social and organizational networks. Her work blends rigorous field-experiment design with scalable ML tooling—evidenced by contributions to the ONNX project improving model type and shape representations. A Tsinghua-trained computer scientist with a PhD from MIT, she has applied her methods at Meta and Microsoft and has a track record of translating theory into practical systems for large-scale experiments. Colleagues describe her as equally at home writing production-grade model code and formalizing causal questions that drive real-world policy and product decisions.
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 (11)
machine-learning10
deep-learning10
onnx10
protobuffer10
protobuf10
python9
pytorch8
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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