Office Hour with Alexander Gray

Expo Hall (Table B)

- Machine learning methodology and best practices – formulating application problems in terms of machine learning, avoiding subtle statistical biases, handling difficult real-world situations, etc.

- Machine learning on large-scale data – scalability of various methods using state-of-the-art algorithms, recent algorithmic possibilities, etc.

Photo of Alexander Gray

Alexander Gray

Skytree, Inc.

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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