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Cloudera

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Date: November 15, 2018 Time: 10:00am PT/1:00pm ET

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Add to Calendar 11/15/2018 10:00 AM 11/15/2018 11:00 AM America/Los_Angeles Federated Learning: ML with Privacy on the Edge Cloudera Fast Forward Labs’ latest research report and prototype introduce Federated Learning, a way to build intelligent systems without moving data. Federated Learning allows you to learn from privacy-sensitive data and edge devices. Attendees will: -Learn what federated learning is, when it's applicable, how it works, and the current landscape of tools and libraries -See a federated learning proof of concept built to solve a predictive maintenance problem -Hear from guests in academia and industry about the future of this exciting field Online Webinar

Cloudera Fast Forward Labs’ latest research report and prototype introduce Federated Learning, a way to build intelligent systems without moving data. Federated Learning allows you to learn from privacy-sensitive data and edge devices. Attendees will:

  • Learn what federated learning is, when it's applicable, how it works, and the current landscape of tools and libraries

  • See a federated learning proof of concept built to solve a predictive maintenance problem

  • Hear from guests in academia and industry about the future of this exciting field

Speakers

Research Engineer, Cloudera Fast Forward Labs

Mike Lee Williams

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Mike Lee Williams is a Research Engineer at Cloudera Fast Forward Labs, where he builds prototypes that bring the latest ideas in machine learning and AI to life and helps Cloudera Fast Forward Labs’ clients understand how to make use of these new technologies. Mike holds a PhD in astrophysics from Oxford.

Assistant Professor in Electrical and Computer Engineering, Carnegie Mellon University

Virginia Smith

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Virginia Smith is an assistant professor in Electrical and Computer Engineering at Carnegie Mellon University, and an affiliated faculty member in the Machine Learning Department. Her research interests are at the intersection of machine learning, optimization, and distributed systems. Prior to CMU, Virginia was a postdoc at Stanford University, received a Ph.D. in Computer Science from UC Berkeley, and obtained undergraduate degrees in Mathematics and Computer Science from the University of Virginia.

PhD student, University of Oxford/OpenMined

Andrew Trask

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Andrew Trask is a PhD student at the University of Oxford where he researches new techniques for privacy preserving deep learning including federated learning, secure multi-party computation, and differential privacy. Andrew has a passion for making complex ideas easy to learn. As such, he is the author of the book Grokking Deep Learning, an instructor in Udacity’s Deep Learning nanodegree program, and the author of popular deep learning blog i am trask. He is also the leader of the OpenMined open source community, a group of over 3,000 researchers, practitioners, and enthusiasts, which extends major deep learning frameworks with open source tools for privacy preserving deep learning.

Head of Federated Learning, Owkin

Eric W. Tramel

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Eric W. Tramel is the head of the federated learning and private ML R&D team at OWKIN, a biotech startup building tools to accelerate clinical research. Prior to joining OWKIN, he served as a postdoc at both École Normale Supérieure and INRIA, where he worked at the interface of statistical physics and problems in machine learning and information theory. He received his Ph.D. and undergraduate degrees from Mississippi State University, where he wrote his dissertation on the topic of compressed sensing of images and video.

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