Yu Xiao
Senior Software Engineer at Google
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolYu Xiao is a Senior Software Engineer based in Seattle with four years of hands-on experience building scalable systems and infrastructure, currently working at Google. He is proficient in Java, C++, JavaScript, and Python, and has strong familiarity with Spring, Tomcat, PostgreSQL, Linux toolchains and embedded Yocto builds. Yu has practical full-stack experience from developing large-scale device troubleshooting automation and transaction-safe admin systems to low-level embedded Linux and kernel cross-compilation in research projects. At Google he contributed to prominent open-source federated learning projects (TensorFlow Federated and Google Research's federated repo), improving Keras integration, metric tracking, and MeasuredProcess utilities. His background combines academic research in image processing and UAV computer vision with production-grade ML and backend engineering, making him effective at bridging research code and reliable infrastructure. Colleagues describe him as a diligent problem-solver who quietly improves observability and automation in complex systems.
4 years of coding experience
2 years of employment as a software developer
Bachelor of Science (B.S.), Electrical and Electronics Engineering, Bachelor of Science (B.S.), Electrical and Electronics Engineering at Fudan UniversityMaster of Science (MS), Major: Electrical and Electronics Engineering, Master of Science (MS), Major: Electrical and Electronics Engineering at Washington University in St. Louis
English, Chinese
Github Skills (12)
model-building10
keras10
machine-learning10
tensorflow10
python10
federated-learning10
modeling10
model-driven10
metric10
model-driven-development10
web-framework9
documentation8
Programming languages (2)
Github contributions (4)
google-parfait/tensorflow-federated
Sep 2021 - Jul 2022
An open-source framework for machine learning and other computations on decentralized data.
Role in this project:
ML Engineer
ML EngineerContributions:5 releases, 38 commits, 16 PRs in 10 months
Contributions summary:Yu contributed to the development and maintenance of the TensorFlow Federated (TFF) framework, which is focused on machine learning on decentralized data. Their work includes implementing new features, such as providing warnings when Keras models with batch normalization layers are used. The user also created helper functions to compose and concatenate MeasuredProcesses, along with addressing issues related to Keras model integration within the TFF framework. The user updated the documentation regarding the usage of Keras BatchNorm in TFF.
pytorchdeep-learningmachine-learningsecure-computationfederated-learning
google-research/federated
Nov 2021 - Apr 2022
A collection of Google research projects related to Federated Learning and Federated Analytics.
Role in this project:ML Engineer
ML EngineerContributions:6 commits in 4 months
Contributions summary:Yu primarily contributed to the development and maintenance of machine learning models within the federated learning framework. Their work involved modifying and enhancing custom Keras metrics to improve integration with TFF (TensorFlow Federated). They also addressed deprecated attributes related to model reporting and integrated a `reset_metrics` method for accurate metric tracking in federated evaluation. The changes span core components like model definitions and evaluation utilities.
analyticsfederated-analyticsmachine-learningkubernetesfederated-learning
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