Alexander Gray

Alexander Gray
CEO, Skytree, Inc.

Website | @skytreeHQ

Dr. Gray obtained degrees in Applied Mathematics and Computer Science from Berkeley and a PhD in Computer Science from Carnegie Mellon, and is a tenured 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 Science journal’s Top Breakthrough of 2003, and have won a number of research awards. He is a member of 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 top research conferences and agencies.

Sessions

Data Science Ballroom AB
Alexander Gray (Skytree, Inc.)
Average rating: ***..
(3.30, 10 ratings)
Given a machine learning (ML) problem, which method(s) should you use, and how does big data affect your choices? I will discuss some principles derived from decades of theory and practice, illustrated through real-world ML success stories in medicine, marketing, financial services, and astronomy. Read more.
Design Ballroom CD
Alexander Gray (Skytree, Inc.), Monica Rogati (Data Natives), Julie Steele (Manifold), Douglas van der Molen (ClearStory Data)
Average rating: ****.
(4.33, 3 ratings)
The Great Debate series returns to Strata. In this Oxford-style debate, two opposing teams take opposing positions. We poll the audience, and the teams try to sway opinions. It'll be a fast-paced, sometimes irreverent look at some of the core challenges of putting data to work. Read more.

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@oreilly.com

Press and Media

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