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Jeff Dean - Invited Talk
Jeff Dean

Sat Dec 03 06:40 AM -- 07:30 AM (PST) @
in Machine Learning for Systems »

In this talk, I'll touch on the myriad ways that machine learning is being used to dramatically rethink how computer systems are approached. I'll highlight research work in domains covering a variety of problems in ASIC chip design, computer architecture, distributed systems, database systems, compilers, content delivery systems, and more. I'll also highlight how building simple interfaces that allow "learned choices" to be integrated into the middle of existing hand coded computer software can dramatically ease the breadth and ease with which machine learning can be applied to a variety of different kinds of decisions, including many decisions at the core of computer systems. This talk presents work by a great number of Google Research colleagues and is meant to be an overview of the exciting advances in applying ML to computer systems problems.

Author Information

Jeff Dean (Google Research)

Jeff joined Google in 1999 and is currently a Google Senior Fellow. He currently leads Google's Research and Health divisions, where he co-founded the Google Brain team. He has co-designed/implemented multiple generations of Google's distributed machine learning systems for neural network training and inference, as well as multiple generations of Google's crawling, indexing, and query serving systems, and major pieces of Google's initial advertising and AdSense for Content systems. He is also a co-designer and co-implementor of Google's distributed computing infrastructure, including the MapReduce, BigTable and Spanner systems, protocol buffers, LevelDB, systems infrastructure for statistical machine translation, and a variety of internal and external libraries and developer tools. He received a Ph.D. in Computer Science from the University of Washington in 1996, working with Craig Chambers on compiler techniques for object-oriented languages. He is a Fellow of the ACM, a Fellow of the AAAS, a member of the U.S. National Academy of Engineering, and a recipient of the Mark Weiser Award and the ACM Prize in Computing.

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