Alexander 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 work aims to ultimately scale up all of the major methods of machine learning (ML) to massive datasets, and includes a number of the current state-of-the-art algorithms for several key bottlenecks. He began working on this problem at NASA in 1993 (long before the currently fashionable talk of “big data”). His algorithms helped enable high-profile scientific results including the Science Journal's Top Breakthrough of 2003, and have won a number of awards. He served on the National Academy of Sciences (NAS) Committee on the Analysis of Massive Data, is a NAS Kavli Scholar, and frequently gives invited tutorial lectures on massive-scale ML at premier meetings and agencies. He co-founded Skytree, Inc., "the Machine Learning Company", to bring the edge of algorithmic and statistical research to industry.
- Theoretical Foundations of Big Data Analysis, Fall 2013. Visiting Scientist.