Programme

Preliminary schedule, speakers are confirmed but exact times may change and their talk titles may be updated.

07:30-07:40
Welcome & Session I
Chair: Dr. Sarah Bridle, UCL

07:40-08:20
Prof. Dr. Robert Lupton,
Princeton University
Invited talk: Astronomical Data
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08:20-08:50
Spotlight Session I
Chair: Michael Hirsch, UCL
Very short talks by poster contributors
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08:50-09:20
Coffee







Sesstion II
Chair: Dr. Rob Fergus, NYU

09:20-10:00
Prof. Dr. David Hogg,
New York University
Invited talk: Theories of Everything
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10:00-10:30
Spotlight Session II
Chair: Michael Hirsch, UCL
Very short talks by poster contributors
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10:30-16:00
Break
Unworkshop






Session III
Chair: Dr. Iain Murray, Edinburgh

16:00-16:40
Prof. Dr. Alexandre Refregier,
ETH Zurich
Invited talk: Challenges in Cosmic Shear






Session IV
Chair: Dr. Stefan Harmeling, MPI for Intelligent Systems

16:40-17:20
Prof. Dr. Jean-Luc Starck,
CEA Saclay Paris
Invited talk: Learning How to Reconstruct the Cosmic
Microwave Background
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17:20-18:00
Coffee






18:00-19:00
Panel Discussion
Opportunities for cosmology to meet machine learning

Invited panel: Bernhard Schölkopf, Iain Murray, Rob Fergus,
Neil Lawrence, David Hogg, Alexandre Refregier, Jean-Luc Starck, Robert Lupton





19:00-19:15
Closing Remarks
Dr. Phil Marshall, University of Oxford
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Abstracts:


Robert Lupton (Princeton)
Astronomical Data <pdf>
I shall introduce astronomical data, using examples from the Sloan Digital Sky Survey. In particular, I'll discuss the problem of finding very rare and very distant quasars in multi-band CCD data. I'll then discuss the statistical models used in analysing these data, and briefly mention the flood of data that will inundate astronomers over the next 10 years.


David Hogg, NYU
Theories of Everything <pdf>
Cosmology, at the present day, works with static catalogs (of, say, galaxies) and point estimates of fundamental physical quantities (of, for example, two-point functions, on which most cosmological science is based). Next-generation cosmological experiments are looking for ever more subtle effects in larger and larger data sets; they will not provide all the cosmological information of which they are capable unless we can develop probabilistic approaches that transmit as best as possible all the information in the raw data to the quantities of interest (for example, the spectrum of primordial density fluctuations or the mass of the dark-matter particle). I will show some baby steps towards creating a probabilistic generative model of the full system, with cosmological parameters at the top and raw image pixels at the bottom. Parts of this work have been performed in collaboration with Fergus (NYU), Lang (Princeton), Marshall (Oxford), and Murray (Edinburgh).


Alexandre Refregier, ETH Zurich
Challenges in Cosmic Shear
Recent observations have shown that the Universe is dominated by two mysterious components, Dark Matter and Dark Energy. Their nature pose some of the most pressing questions in fundamental physics today.Weak gravitational lensing, or 'cosmic' shear', is a powerful technique to probe these dark components. We will first review the principles of cosmic shear and its current observational status. We will describe the future surveys which will be available for cosmic shear studies. We will then highlight key challenges in data analysis which need to be met for the potential of these future surveys to be fully realised.


Jean-Luc Starck, CEA Saclay Paris
Learning How to Reconstruct the Cosmic Microwave Background <pdf>
Cosmic Microwave Background (CMB) temperature anisotropies and polarisation measurements have been one of the key cosmological probes to establish the current cosmological model. The ESA’s PLANCK mission is designed to deliver full-sky coverage, low-noise level, high resolution temperature and polarisations maps. We will briefly review some of the key problem of the PLANCK data analysis, and we will present how sparsity can be used to analyze such data set, especially for learning how to separate the CMB from other sky components.