H Mcmahan
Principal Research Scientist at Google
Seattle, Washington, United States
Summary
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Rockstar🎓
Top SchoolH Mcmahan is a Principal Research Scientist at Google based in Seattle, bringing about seven years of focused industry experience underpinned by a PhD in Computer Science from Carnegie Mellon and a strong mathematics background from Whitman College. He specializes in federated learning and decentralized ML systems, contributing to prominent open-source work such as tensorflow-federated where he introduced Model abstractions and client-side training logic for federated averaging. At Google he has advanced from Senior Staff to Principal research ranks, combining deep research rigor with practical engineering to ship scalable privacy-preserving learning algorithms. Known for bridging theoretical techniques and production-ready implementations, he routinely translates complex distributed learning concepts into usable frameworks that accelerate real-world ML on decentralized data.
7 years of coding experience
17 years of employment as a software developer
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at Carnegie Mellon UniversityBachelor of Arts - BA Mathematics, Bachelor of Arts - BA Mathematics at Whitman College
Github Skills (7)
machine-learning10
tensorflow10
trainings10
python10
federated-learning10
modeling10
deep-learning9
Programming languages (3)
Github contributions (5)
google-parfait/tensorflow-federated
Nov 2018 - Dec 2022
An open-source framework for machine learning and other computations on decentralized data.
Role in this project:
ML Engineer
ML EngineerContributions:1 release, 86 commits, 1 tag in 4 years 2 months
Contributions summary:H's commits primarily involve the creation and modification of files related to model definition and training within the `tensorflow-federated` repository. Their contributions include the introduction of a new `Model` abstraction, implementation of example models, and modifications to client-side TensorFlow computations for federated learning algorithms such as Federated Averaging. These changes demonstrate a focus on defining and preparing models for federated training scenarios.
pytorchdeep-learningmachine-learningsecure-computationfederated-learning
ZeitgeistQIAN/federated
Apr 2019 - May 2019
A framework for implementing federated learning
Contributions:5 commits in 1 month
pytorchdeep-learningmachine-learningfederated-learningfederated
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