Watch this space: emerging trends and techniques that could transform physics and astronomy

• Kyle Stuart Cranmer (New York University (US))

Inter-experimental Machine Learning at the LHC

• Sergei Gleyzer (University of Florida (US))

Large Scale Machine Learning in Astronomy

• Gieseke Fabian

GRID Processing of LOFAR spectroscopic and imaging surveys

• Natalie Danezi (SurfSara)

HPC Processing and Deep Learning Techniques for Classifying Radio Bursts

• Joeri van Leeuwen

Collaborative machine learning for science with OpenML

• Joaquin Vanschoren (TU Eindhoven)

Amsterdam Data Science

• Maarten de Rijke

eScience

• Wilco Hazeleger

SurfSara

• Maithili Kalamkar

Determining the origin of the Galactic Center excess using convolutional neural networks

• Hendriks Luc

Scaling up in complexity

• Zahari Dimitrov Kassabov Zaharieva (University of Turin)

Good fits of gamma-ray data with high-dimensional modelling

• Emma Storm (UvA)

Generalizing LHC limits on Supersymmetry with Machine Learning

• Bob Stienen (IMAPP, Radboud University Nijmegen)

Wrap-up and next steps

• Juan Rojo (VU Amsterdam and Nikhef)