Alexander Gray
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Alexander Gray received bachelor's degrees in Applied Mathematics and Computer Science from the University of California, Berkeley and a PhD in Computer Science from Carnegie Mellon University, and is an Associate Professor in the College of Computing at Georgia Tech heading up its new Center for Big Data Analytics and Machine Learning, and CTO of Skytree Inc., "the machine learning company". His research group, the FASTlab, aims to algorithmically scale up all of the major practical methods of machine learning to massive datasets and has developed a number of fast algorithms for several key problems, as well as new statistical methodology. He began working with massive astronomical datasets in 1993 (long before the current fashionable talk of ³big data²) at NASA's Jet Propulsion Laboratory in its Machine Learning Systems Group. High-profile applications of his large-scale ML algorithms have been described in staff written articles in Science and Nature, including contributions to work selected by Science as the Top Scientific Breakthrough of 2003. His work has won or been nominated for seven best paper awards and he is a National Science Foundation CAREER Award recipient, National Academy of Sciences Kavli Scholar, and frequent advisor on the topic of big-data machine learning at research conferences, government agencies, and corporations, recently serving on the National Academy of Sciences Committee on the Analysis of Massive Data.


flag Anomaly detection with density estimation trees
as author at  KDD 2017 Workshops,
  invited talk
flag Modern Nonparametric Statistics on Modern Big Data
as author at  Modern Nonparametric Methods in Machine Learning,
invited talk
locked flag Machine Learning on (Astronomically) Large Datasets
as author at  28th Conference on Uncertainty in Artificial Intelligence (UAI), Catalina Islands 2012,
flag Measurement Errors in Astrostatistics
as author at  Cosmology meets Machine Learning,
flag Efficient Estimation of N-point Spatial Statistics
as author at  Cosmology meets Machine Learning,