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Machine Learning and Astronomy lecture

A heads up all - NU's CIERA (Center for Interdisciplinary Education and Research in Astrophysics) is hosting a talk this Wednesday at the intersection of ML and Astronomy to be given by Prof. Josh Bloom - see more below!!


"Inference in Time Domain Astrophysics" 


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 large-scale 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 pseudo-spectrograph will be discussed.


Professor Bloom is a self-described "data-driven scientist", known in the astronomy community for developing machine-learning techniques to discover and study transient sources such as gamma-ray bursts, supernovae, and tidal disruption events.  

He is also the co-PI of the new UC Berkeley Institute for Data Science (BIDS), opening this Spring.

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