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IEEE Symposium on Computational Intelligence for Astroinformatics

The explosion of data richness and complexity have resulted in the relatively new field of Astroinformatics: an interdisciplinary area of research where astronomers, mathematicians and computer scientists collaborate to solve problems in astronomy through the application of techniques developed in data science.The symposium aims to capture the baseline, set the tempo for future research in India and abroad and prepare a scholastic primer that would serve as a standard document for future research. Scalable and fast applications to image processing, time-series analysis, deep and wide networks, as well as fusion methods are driving progress in many areas. We hope to learn about methods that are applicable to astroinformatics but are not currently used, and also making CS practitioners aware of the interesting problems that complex astronomy datasets provide. We welcome original and unpublished contributions (no more than 8 pages including figures, tables and bibliography, in IEEE two-column format) that discuss new developments in efficient models for complex computer experiments and data analytic techniques which can be used in astronomical data analysis as well as related branches in physical, statistical and computational sciences.
When 18 November 2018 11:25 PM to
21 November 2018 11:25 PM
Where Bengaluru IN
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Topics: Specific topics of interest include, but are not limited to: 

  •    Exoplanets (discovery, classification etc.)
  •    Classification of transients (Galactic and extragalactic)
  •    Multi-messenger astronomy aided by Machine learning
  •    Deep learning in astronomy
  •    Gravitational Wave data analysis
  •    MCMC on big data
  •    Imaging Problems in Astronomy
  •    Big Data Solar Astronomy
  •    Statistical Machine Learning (including Bayesian methods)
  •    Meta-heuristic and Evolutionary Clustering methods and applications in Astronomy
  •    Astronomical time series analysis
  •    CI based interpolation methods for data fitting problems

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