Cosmology meets Machine Learning

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.

The objectives of this workshop are two-fold:

• 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.
  • 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 held 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.

Workshop homepage: http://cmml-nips2011.wikispaces.com/