Yihong Gu
Director Of Engineering at Google
United States
Summary
🤩
Rockstar🎓
Top SchoolYihong Gu is a seasoned engineering leader with over 20 years in software and a decade of senior leadership experience, currently directing ChromeOS Engineering Productivity at Google. He combines deep hands-on expertise—from early software engineering and SDET work at Microsoft and CacheFlow to managing large-scale developer productivity initiatives—while maintaining technical fluency in ML tooling. As an ORFE Ph.D. student at Princeton and an active contributor to probabilistic deep learning (notably implementing normalizing flows in the well-known zhusuan library), he bridges rigorous research and production engineering. Yihong’s background in materials science and advanced CS education gives him a cross-disciplinary perspective that informs pragmatic, data-driven decisions. Colleagues describe him as a pragmatic problem-solver who brings both academic rigor and operational focus to complex software ecosystems.
10 years of coding experience
1 year of employment as a software developer
MS Computer Science, MS Computer Science at University of Illinois Urbana-ChampaignMS Materials Science & Engineering, MS Materials Science & Engineering at Tsinghua University
Github Skills (8)
generative-model10
machine-learning10
probabilistic-programming10
deep-learning10
tensorflow10
python10
bayesian-inference9
bayesian9
Programming languages (2)
Github contributions (5)
thu-ml/zhusuan
Mar 2017 - Jan 2018
A probabilistic programming library for Bayesian deep learning, generative models, based on Tensorflow
Role in this project:
ML Engineer
ML EngineerContributions:13 commits, 2 PRs, 4 pushes in 9 months
Contributions summary:Yihong primarily focused on developing and modifying normalizing flows within the probabilistic programming library, zhusuan. Their contributions involved implementing and testing specific flow architectures, including planar and inverse autoregressive flows, as well as ensuring proper shape handling for these transformations. The commits also included bug fixes and the addition of new test cases to validate the functionality and numerical stability of the implemented flow methods.
information-theorydeep-learningbayesian-inferencegraphical-modelsmachine-learning
wmyw96/convex-cnn-tf
Dec 2019 - Jun 2020
Contributions:59 commits, 41 pushes, 1 branch in 5 months
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