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Cosmology meets Machine Learning
Michael Hirsch · Sarah Bridle · Bernhard Schölkopf · Phil Marshall · Stefan Harmeling · Mark Girolami

Thu Dec 15 10:30 PM -- 11:00 AM (PST) @ Melia Sierra Nevada: Monachil
Event URL: http://webdav.is.mpg.de/pixel/cmml-nips2011.html »

Cosmology aims at the understanding of the universe and its evolution through scientific observation and experiment and hence addresses one of the most profound questions of human mankind. With the establishment of robotic telescopes and wide sky surveys cosmology already now faces the challenge of evaluating vast amount of data. Multiple projects will image large fractions of the sky in the next decade, for example the Dark Energy Survey will culminate in a catalogue of 300 million objects extracted from peta-bytes of observational data. The importance of automatic data evaluation and analysis tools for the success of these surveys is undisputed.

Many problems in modern cosmological data analysis are tightly related to fundamental problems in machine learning, such as classifying stars and galaxies, and cluster finding of dense galaxy populations. Other typical problems include data reduction, probability density estimation, how to deal with missing data and how to combine data from different surveys.

An increasing part of modern cosmology aims at the development of new statistical data analysis tools and the study of their behaviour and systematics often not aware of recent developments in machine learning and computational statistics.

Therefore, the objectives of this workshop are two-fold:

(i) The workshop aims to bring together experts from the Machine Learning and Computational Statistics community with experts in the field of cosmology to promote, discuss and explore the use of machine learning techniques in data analysis problems in cosmology and to advance the state of the art.

(ii) By presenting current approaches, their possible limitations, and open data analysis problems in cosmology to the NIPS community, this workshop aims to encourage scientific exchange and to foster collaborations among the workshop participants.

The workshop is proposed as a one-day workshop organised jointly by experts in the field of empirical inference and cosmology. The target group of participants are researchers working in the field of cosmological data analysis as well as researchers from the whole NIPS community sharing the interest in real-world applications in a fascinating, fast-progressing field of fundamental research. Due to the mixed participation of computer scientists and cosmologists the invited speakers will be asked to give talks with tutorial character and make the covered material accessible for both computer scientists and cosmologists.

Author Information

Michael Hirsch (Max Planck Institute for Intelligent Systems)
Sarah Bridle (UCL)
Bernhard Schölkopf (MPI for Intelligent Systems)

Bernhard Scholkopf received degrees in mathematics (London) and physics (Tubingen), and a doctorate in computer science from the Technical University Berlin. He has researched at AT&T Bell Labs, at GMD FIRST, Berlin, at the Australian National University, Canberra, and at Microsoft Research Cambridge (UK). In 2001, he was appointed scientific member of the Max Planck Society and director at the MPI for Biological Cybernetics; in 2010 he founded the Max Planck Institute for Intelligent Systems. For further information, see www.kyb.tuebingen.mpg.de/~bs.

Phil Marshall (University of Oxford)
Stefan Harmeling (Heinrich-Heine-Universität Düsseldorf)
Mark Girolami (University of Warwick, The Alan Turing Institute for Data Science)

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