Paul Chiang
Software Engineer at Google
Zurich, Zurich, Switzerland
Summary
🤩
Rockstar🎓
Top SchoolPaul Chiang is a seasoned software engineer with 9 years of experience building secure, scalable systems and a strong background in cloud and edge computing. Currently at Google in Zurich, he previously spent several years at Microsoft where he helped secure off-site Edge networks and scaled a verification service to support 800+ users and over a million test runs per month. His work spans low-level system design, distributed storage and cloud migrations to applied machine learning—he has contributed fixes and robustness improvements to the popular Keras library related to model loading and optimizer state restoration. Paul holds advanced computer science degrees from Stanford and National Chiao Tung University, reflecting deep academic training paired with production engineering. Colleagues would describe him as practical and detail-oriented, able to take projects end-to-end from design through operations. An interesting quirk: he has a track record of improving tooling and error messaging to make complex systems more maintainable and user-friendly.
9 years of coding experience
4 years of employment as a software developer
M.S, Network Engineering, M.S, Network Engineering at National Chiao Tung UniversityM.S., Computer Science, M.S., Computer Science at Stanford University
English, Chinese
Github Skills (6)
neural-network10
keras10
machine-learning10
deep-learning10
tensorflow10
python10
Programming languages (4)
Github contributions (5)
keras-team/keras
Sep 2021 - Dec 2021
Deep Learning for humans
Role in this project:
ML Engineer
ML EngineerContributions:11 commits in 3 months
Contributions summary:Paul primarily contributed to the Keras deep learning library by addressing issues related to model loading and optimization. They improved error messages in `load_model`, ensuring more informative feedback. Furthermore, the user modified the optimizer code to return slot variables correctly for proper restoration. They also updated the code to use `isdir()` to correctly identify the file type and fixed issues in saved model loading.
pythondata-sciencedeep-learningneural-networksmachine-learning
pcish/yamdc
May 2019 - Nov 2019
Contributions:4 pushes in 6 months
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