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    Keith Rush

    Software Engineer in Machine Learning at Google

    Keith Rush is a Staff Research Scientist at Google, specializing in distributed and private learning with a background in mathematics.12 He holds a Ph.D. in Mathematics from the University of Wisconsin-Madison.2

    Rush's research areas include:

    1. Algorithms and Theory
    2. Distributed Systems
    3. Machine Learning

    His work focuses on federated learning, adaptive optimization methods, and differentially private model personalization.1 Some of his notable publications include:

    • "Does Federated Dropout actually work?" (2022)
    • "Adaptive Federated Optimization" (2021)
    • "Federated Reconstruction: Partially Local Federated Learning" (2021)
    • "Differentially Private Model Personalization" (2021)

    Keith Rush has made significant contributions to the field of federated learning, particularly in addressing challenges related to client heterogeneity, communication efficiency, and privacy constraints in distributed machine learning settings.1

    Highlights

    Jan 1 · research.google
    Keith Rush - Google Research

    Related Questions

    What are Keith Rush's main research areas in machine learning?
    How does Keith Rush's work contribute to distributed and private learning?
    What are some notable publications authored by Keith Rush?
    Can you provide more details about Keith Rush's role at Google?
    What is Federated Dropout, and how does it relate to Keith Rush's research?
    Keith Rush
    Keith Rush, photo 1
    Keith Rush, photo 2
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    Location

    Seattle, Washington, United States