Keith Rush
Staff Research Scientist at Google DeepMind
Seattle, Washington, United States
Summary
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Rockstar🎓
Top SchoolKeith Rush is a Staff Research Scientist at Google DeepMind with eight years of experience bridging rigorous mathematics and large-scale distributed machine learning. He holds a Ph.D. in Mathematics from the University of Wisconsin–Madison and a BA in Mathematics from Penn, and his work spans algorithms, systems, frameworks, and languages for federated and distributed ML. As a core developer on TensorFlow Federated he implemented key building blocks for TensorFlow-backed federated computations, improving sequence operations and support for unknown tensor shapes in widely used open-source tooling. Prior roles include research scientist and ML engineering positions at Google and leadership of data science at the Milwaukee Brewers, where he translated business questions into production ML systems. Based in Seattle, Keith combines deep theoretical training in analysis and probability with hands-on engineering that moves cutting-edge research into practical, scalable systems. A mathematician at heart—his GitHub motto, “It’s all mathematics,” reflects a consistent through-line from orthogonal polynomials research to federated learning infrastructure.
8 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Mathematics, Doctor of Philosophy (Ph.D.) Mathematics at University of Wisconsin-MadisonFar Brook SchoolBachelor of Arts (B.A.) Mathematics, Bachelor of Arts (B.A.) Mathematics at University of Pennsylvania
English, French
Stackoverflow
1,380reputation
29kreached
77answers
1question
tensorflow
top-5%
Github Skills (9)
machine-learning10
tensorflow10
federated-learning10
python10
deep-learning7
neural-network6
keras6
out-of-memory6
mnist6
Programming languages (3)
Github contributions (5)
google-parfait/tensorflow-federated
Dec 2018 - Jan 2023
An open-source framework for machine learning and other computations on decentralized data.
Role in this project:
ML Engineer
ML EngineerContributions:7 releases, 4 reviews, 681 commits in 4 years 2 months
Contributions summary:Keith implemented building blocks for construction of TensorFlow (TF) constants and improved sequence computation of machine learning (ML) models by implementing sequence operations, and introducing support for parameters with unknown shape dimensions in TF. The code changes involved integration of various TFF (TensorFlow Federated) components, including those for evaluation and metrics, within the broader federated learning framework, in addition to introducing code to address specific errors in the testing of these implementations. The user also worked on enabling the use of TensorFlow-backed features in the EMNIST data-based research.
pytorchdeep-learningmachine-learningsecure-computationfederated-learning
jkr26/TopologyLearning
Oct 2017 - Oct 2018
Contributions:2 PRs, 22 pushes, 5 branches in 1 year 1 month
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