Do you want to achieve best-in-class results with high-performance learning in your application? Join Alexander and Leland to find out how to do just that, and to discuss:
Alexander Gray is an associate professor at Georgia Tech and the CTO of Skytree, Inc. His research focuses on scaling up all of the major practical methods of machine learning (ML) to massive datasets. Alex 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.
Alex served on 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. Alexander has degrees in applied mathematics and computer science from UC Berkeley and a PhD in computer science from Carnegie Mellon.
Leland Wilkinson is currently adjunct professor of computer science at the University of Illinois at Chicago. Previously, he was adjunct professor of statistics at Northwestern University and President of SYSTAT Inc., a statistical software company he founded in 1984. Wilkinson is a fellow of the American Statistical Association, an elected member of the International Statistical Institute, and a fellow of the American Association for the Advancement of Science. He was vice-chair of the board of the National Institute of Statistical Sciences and a member of the Committee on Applied and Theoretical Statistics at the National Academy of Sciences. Wilkinson recently served on the NAS Panel on Developing Science, Technology, and Innovation Indicators for the Future. His projects have included books, journal articles, the original SYSTAT statistics package, numerous open-source software packages, and patents in visualization and distributed computing. He has an A.B. from Harvard University, an S.T.B. degree from Harvard Divinity School, and a Ph.D. from Yale.
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