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IPA workshop on Machine Learning for particle physics and astrophysics (IPA-ML)

Mar 20 – 22, 2023
ETH Zurich
Europe/Zurich timezone

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Contribution List

33 out of 33 displayed
  1. 41. Welcome
    Prof. Davide Sgalaberna (ETH Zurich)
    3/20/23, 10:00 AM
  2. 10. Physics and machine learning: an overview
    Dr Saul Alonso Monsalve (ETH Zurich)
    3/20/23, 10:30 AM
  3. 14. Machine learning lecture
    Dr Mauro Donega (ETH Zurich)
    3/20/23, 1:30 PM
  4. 45. Machine learning (BDT) tutorial
    Simone Pigazzini (ETH Zurich), Massimiliano Galli
    3/20/23, 3:15 PM
  5. 15. Deep learning tutorial
    Dr Saul Alonso Monsalve (ETH Zurich)
    3/20/23, 4:15 PM

    In this tutorial, you'll learn how to use deep learning to perform particle identification. We'll cover the basics of neural networks and how to build them using Python and TensorFlow. Participants will train their own neural networks using a dataset of particle tracks, and learn how to evaluate their performance. To participate, you'll need a Google account for [Google...

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  6. 16. Introduction on neutrino experiments workflow with emphasis on challenges
    Prof. Davide Sgalaberna (ETH Zurich)
    3/21/23, 9:00 AM
  7. 21. Image reconstruction in a 3D plastic scintillator detector using deep learning
    Dr Saul Alonso Monsalve (ETH Zurich)
    3/21/23, 9:20 AM
  8. 17. Neutrino interaction classification and transfer learning
    Dr Leigh Whitehead (University of Cambridge)
    3/21/23, 9:40 AM
  9. 19. Event filtering and mitigation of simulation biases
    Dr Marta Babicz (University of Zurich)
    3/21/23, 10:00 AM
  10. 20. Event reweighting and generative models in neutrino experiments
    Dr Cristovao Vilela (CERN)
    3/21/23, 10:50 AM
  11. 18. Multi-task data reconstruction chain for imaging detectors in neutrino experiments
    Prof. Kazuhiro Terao (SLAC)
    3/21/23, 11:10 AM
  12. 22. Large Scale Structure Cosmology with Artificial Intelligence
    Dr Tomasz Kacprzak (ETH Zurich)
    3/21/23, 11:30 AM
  13. 23. Cosmological constraints from combined probes of large scale structure with deep learning
    Arne Thomsen (ETH Zurich)
    3/21/23, 11:50 AM
  14. 44. Background rejection in Cherenkov Telescopes
    Prof. Adrian Biland (ETH Zurich)
    3/21/23, 1:20 PM
  15. 25. New generative AI models
    Luca Biggio (ETH Zurich)
    3/21/23, 1:40 PM
  16. 26. Geometric Learning/ Physics-informed AI for cosmology
    Dr Tilman Troester (ETH Zurich)
    3/21/23, 2:00 PM
  17. 24. PointNets for galaxy redshift surveys
    Sotirios Anagnostidis (ETH Zurich)
    3/21/23, 2:20 PM
  18. 27. Introduction on LHC experiments workflow with emphasis on challenges
    Prof. Annapaola de Cosa (ETH Zurich)
    3/21/23, 3:10 PM
  19. 30. Object identification and reconstruction
    Dr Alessandro Calandri (ETH Zurich)
    3/21/23, 3:30 PM
  20. 28. Ultrafast ML inference for triggering
    Dr Thea Aarrestad (ETH Zurich)
    3/21/23, 3:50 PM
  21. 29. Machine learning for data quality monitoring
    Roberto Seidita
    3/21/23, 4:10 PM
  22. 34. Probabilistic models in ML for HEP
    Davide Valsecchi (ETH Zurich)
    3/21/23, 4:30 PM
  23. 31. Data-MC matching
    Massimiliano Galli
    3/22/23, 9:00 AM
  24. 32. Analysis techniques
    Florian Eble
    3/22/23, 9:20 AM
  25. 35. Quantum machine learning
    Vasilis Belis (ETH Zurich)
    3/22/23, 10:00 AM
  26. 37. Machine learning applications
    Dr Mauro Verzetti (Google)
    3/22/23, 10:50 AM
  27. 36. Robotics, event-based imaging
    Prof. Davide Scaramuzza (ETH Zurich)
    3/22/23, 11:30 AM
  28. 40. Knowledge distillation
    Patrick Odagiu (ETH Zurich)
    3/22/23, 12:00 PM
  29. 42. Round table
    3/22/23, 1:30 PM
  30. 43. Closing
    Prof. Annapaola de Cosa
    3/22/23, 2:45 PM
  31. 33. Anomaly detection
    Roberto Seidita (ETH Zurich)
  32. 39. Encoding/decoding
    Doruk Efe Gökmen
  33. 38. Normalizing flows
    Javad Komijani