This 1-day workshop aims to bring together physicists and astronomers in Amsterdam who develop tools to analyse and interpret big data, such as machine learning tools, neural networks and advanced statistical methods. We also aim to review the computer resources available for the exploitation of these tools. In the morning sessions, we will have introductory talks on Machine Learning and Big Data tools specifically tailored to physicists and astronomers, including concrete examples and applications. We will also have contributed talks from local scientists who successfully apply big data tools to specific scientific problems in physics and astronomy. During the extended lunch break, there will be ample time for discussions and networking, including interactive demos and demonstrations, as well as stations and booths for discussing specific issues and to allow local research institutes and companies to introduce themselves to the participants. In the afternoon session we will have institutional talks from Amsterdam Data Science, eScience and SurfSara, as well as a second set of focused short contributed talks to allow the maximum possible number of participants to introduce themselves to the Amsterdam Machine Learning / Big Data tools community.
Confirmed plenary speakers include
- Kyle Cranmer (New York University)
- Natalie Danezi (SurfSara)
- Wilco Hazeleger (eScience)
- Fabian Gieseke (University of Copenhagen)
- Sergei Gleyzer (University of Florida and IML Working Group)
- Maithili Kalamkar (SurfSara)
- Joeri van Leuween (ASTRON)
- Maarten de Rijke (Amsterdam Data Science)
- Joaquin Vanschoren (TU Eindhoven)
This event is partially supported by
- Amsterdam eScience center
- European Research Council, Starting Grant ``PDF4BSM''
- Radboud University Nijmegen