22-26 June 2020
Lund University
Europe/Stockholm timezone

We are happy to announce that the MCnet Machine Learning training event will be held online from Lund University, June 22nd-June 26th 2020.

The event provides a five day course of lectures, talks and tutorials on Machine Learning with applications from particle physics as well as industry.

This is a training event within the MCnetITN3 Innovative Training Network, primarily directed to students and early stage postdocs affiliated with MCnet. Applications from outside of MCnet are welcome and will be considered if places are available. We aim at approximately 30 students, and the event is free of charge.

Our core sessions comprise a series of introductory lectures given by Professor Mattias Ohlsson (Lund University). Our preliminary list of lecturers/speakers include:

  • Historical notes - Carsten Peterson (Computational Biology, Lund)
  • Introduction to machine learning - Mattias Ohlsson (Computational Biology, Lund)
  • Machine learning in high energy physics - Ben Nachman (LBNL, Berkeley)
  • Machine learning for image analysis - Niclas Danielsson (Axis, Lund)
  • How to GAN LHC events - Anja Butter (Heidelberg),
  • Accelerating HEP theory with ML models - Stefano Carrazza (Milan)
  • How to implement denoising and variational autoencoders - Najmeh Abiri (IT-University Copenhagen Computer Science)
  • Bayesian deep probabilistic differentiable programming: A scientific approach to AI - Michael Green (Desupervised, Copenhagen)
  • Can we “machine-learn” the next standard model?  - Wolfgang Waltenberger (Vienna)
  • Towards the autonomous machine learning fueled supply chain - Malte Tichy (Blue Yonder, Hamburg)
  • Outlook on ML in HEP - Tilman Plehn (Heidelberg)


Organised by:

Image result for mcnet logo

Lund University

MCnetITN3 is funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 722104.                                                                          


Malin Sjödahl, Stefan Prestel, Ann Durie