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Big data tools for physics and astronomy

19 June 2017
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Europe/Zurich timezone

Contribution List

16 out of 16 displayed
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  1. 3. Watch this space: emerging trends and techniques that could transform physics and astronomy
    Kyle Stuart Cranmer (New York University (US))
    19/06/2017, 09:00
  2. 13. Inter-experimental Machine Learning at the LHC
    Dr Sergei Gleyzer (University of Florida (US))
    19/06/2017, 09:45
  3. 15. Large Scale Machine Learning in Astronomy
    Gieseke Fabian
    19/06/2017, 10:15
  4. 17. GRID Processing of LOFAR spectroscopic and imaging surveys
    Natalie Danezi (SurfSara)
    19/06/2017, 11:05
  5. 18. HPC Processing and Deep Learning Techniques for Classifying Radio Bursts
    Joeri van Leeuwen
    19/06/2017, 11:35
  6. 14. Collaborative machine learning for science with OpenML
    Prof. Joaquin Vanschoren (TU Eindhoven)
    19/06/2017, 12:05
  7. 4. Amsterdam Data Science
    Maarten de Rijke
    19/06/2017, 14:00
  8. 5. eScience
    Wilco Hazeleger
    19/06/2017, 14:30
  9. 6. SurfSara
    Maithili Kalamkar
    19/06/2017, 15:00
  10. 7. Determining the origin of the Galactic Center excess using convolutional neural networks
    Mr Hendriks Luc
    19/06/2017, 16:00
  11. 8. Scaling up in complexity
    Zahari Dimitrov Kassabov Zaharieva (University of Turin)
    19/06/2017, 16:15
  12. 12. Good fits of gamma-ray data with high-dimensional modelling
    Dr Emma Storm (UvA)
    19/06/2017, 16:30
  13. 9. Generalizing LHC limits on Supersymmetry with Machine Learning
    Mr Bob Stienen (IMAPP, Radboud University Nijmegen)
    19/06/2017, 16:45
  14. 16. Wrap-up and next steps
    Juan Rojo (VU Amsterdam and Nikhef)
    19/06/2017, 17:00
  15. 10. Accelerating the BSM interpretation of LHC data with machine learning
    Mr Sebastian Liem (GRAPPA, University of Amsterdam)
  16. 1. The proton structure with artificial neural networks.
    Dr Juan Rojo (VU Amsterdam and Nikhef)
    Contributed talks

    We present how a model-independent and bias-free determination of the internal structure of the proton can be achieved by means of Artificial Neural Networks.

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