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Opportunities for Applications of Deep Learning in Cosmology

Siamak Ravanbakhsh

University of British Columbia

A primary goal of modern cosmology is to map complex large-scale observations to simple theories. This contrast of scale between theory and data inevitably necessitates a computational approach. This may involve a compressive analysis of observational data, massive simulations, or a search for rare events. In this talk, I will argue that recent advances in machine learning and in particular deep learning can significantly change the current practice in 'all' of these fronts. I will review several of our past and ongoing collaborations and identify exciting opportunities for interdisciplinary research.

Date: Tuesday, 19 February 2019
Time: 15:30
Where: McGill University
  McGill Space Institute (3550 University), Conference Room

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