Dr. Gray obtained degrees in Applied Mathematics and Computer Science from Berkeley and a PhD in Computer Science from Carnegie Mellon, and is an Associate Professor at Georgia Tech. His lab works to scale up all of the major practical methods of machine learning (ML) to massive datasets. He began working on this problem at NASA in 1993 (long before the current fashionable talk of “big data”). His large-scale algorithms helped enable the Top Scientific Breakthrough of 2003, and have won a number of research awards. He is a member of the National Academy of Sciences Committee on the Analysis of Massive Data and frequently gives invited tutorial lectures on massive-scale ML at top research conferences and agencies.
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