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Presented at the 2014 Workshop on Algorithms for Modern Massive Data Sets (MMDS 2014), June 19, 2014 (Berkeley, CA):
The scientific promise of modern astrophysical surveys  from exoplanets to gravity waves  is palpable. Yet extracting insight from the data deluge is neither guaranteed nor trivial: existing paradigms for analysis are already beginning to breakdown under the data velocity. I will describe our efforts to apply statistical machine learning to largescale astronomy datasets both in batch and streaming mode. From the discovery of supernovae to the characterization of tens of thousands of variable stars such approaches are leading the way to novel inference. Specific discoveries concerning precision distance measurements and using LSST as a pseudospectrograph will be discussed.
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