Shanshan Wu
Senior Software Engineer at Google
Austin, Texas, United States
Summary
🤩
Rockstar🎓
Top SchoolShanshan Wu is a Senior Software Engineer based in Austin with a decade of experience building machine learning systems and federated-learning tooling at Google. Her work spans hands-on model development (e.g., adapting MobileNetV2 for federated settings) and designing robust personalization evaluation APIs within TensorFlow Federated, helping advance widely used open-source frameworks. She combines deep academic training—PhD in Electrical and Computer Engineering from UT Austin and top grades from Shanghai Jiao Tong University—with practical production experience from multiple Google roles and research internships. Colleagues rely on her for thoughtful metric design and scalable experiment integration, including contributions to federated analytics experiments like FedAvg and HypCluster. She brings a research-minded rigor to applied engineering, often surfacing subtle metric-finalization and aggregation improvements that improve evaluation fidelity.
10 years of coding experience
10 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Electrical and Computer Engineering (CommNetS track), Doctor of Philosophy (Ph.D.), Electrical and Computer Engineering (CommNetS track) at The University of Texas at AustinBachelor's Degree, Electrical and Computer Engineering, 3.94/4.0, Bachelor's Degree, Electrical and Computer Engineering, 3.94/4.0 at Shanghai Jiao Tong University
Chinese, English
Github Skills (14)
mask-rcnn10
model-building10
keras10
faster-rcnn10
machine-learning10
tensorflow10
federated-learning10
python10
modeling10
model-driven10
model-driven-development10
apidoc9
api9
data-analysis8
Programming languages (1)
Github contributions (5)
google-research/federated
Dec 2020 - Oct 2022
A collection of Google research projects related to Federated Learning and Federated Analytics.
Role in this project:
ML Engineer
ML EngineerContributions:26 commits in 1 year 9 months
Contributions summary:Shanshan primarily focused on developing and integrating machine learning models within the federated learning framework. They contributed to creating and modifying the `MobileNetV2` model, a CNN architecture, for use in federated learning guides and experiments. Their work also involved incorporating new attributes, such as `report_local_unfinalized_metrics` and `metric_finalizers`, into the `MnistModel` and deprecating existing model attributes for enhanced metric reporting. Additionally, the user added experiments related to FedAvg and Finetuning with varying data paucity, along with adding HypCluster trainer within the federated learning framework.
analyticsfederated-analyticsmachine-learningkubernetesfederated-learning
google-parfait/tensorflow-federated
Oct 2019 - Jan 2023
An open-source framework for machine learning and other computations on decentralized data.
Role in this project:ML Engineer
ML EngineerContributions:5 releases, 65 commits, 6 PRs in 3 years 3 months
Contributions summary:Shanshan primarily contributed to the development and refinement of personalization evaluation strategies within the TensorFlow Federated framework. Their work involved creating and implementing APIs for evaluating personalization strategies, including building computations to assess the performance of different approaches. The user also addressed code issues, fixing typos and improving documentation. A key contribution was refactoring of the personalization evaluation code to use a more robust method for handling metrics aggregation.
pytorchdeep-learningmachine-learningsecure-computationfederated-learning
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