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ZPW2026: Machine Learning Techniques for Particle Physics

5–7 Jan 2026
UZH Main Building
Europe/Zurich timezone

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Contribution List

38 out of 38 displayed
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  1. 1. Registration
    05/01/2026, 08:30
  2. 2. Welcome
    05/01/2026, 09:30
  3. 8. Experimental View on Deep Learning for Neutrino experiments
    Davide Sgalaberna (ETH Zurich (CH))
    05/01/2026, 09:35
  4. 9. Algorithmic View on Deep Learning for Accelerator Neutrino Experiments
    Dr Saul Alonso Monsalve (ETH Zurich)
    05/01/2026, 10:15
  5. 5. Coffee break
    05/01/2026, 10:55
  6. 6. Machine Learning in Lattice Field Theory
    William Detmold
    05/01/2026, 11:25
  7. 14. Diffusion Models for Lattice Field Theory
    Prof. Gert Aarts (Swansea University)
    05/01/2026, 12:05
  8. 7. Lunch
    05/01/2026, 12:45
  9. 3. Optimal Transport: from A to B...and beyond
    Prof. Alessio Figalli (ETH)
    05/01/2026, 14:20
  10. 4. Data to theory: how to fuel BSM model building with anomaly detection
    Maurizio Pierini (CERN)
    05/01/2026, 15:00
  11. 10. Coffee break
    05/01/2026, 15:30
  12. 28. Particle flow reconstruction with a learnable, differentiable, efficient ML mode
    Joosep Pata (National Institute of Chemical Physics and Biophysics (EE))
    05/01/2026, 16:00
  13. 26. On Deep Full Event Reconstruction
    Julian Garcia Pardinas (Massachusetts Inst. of Technology (US))
    05/01/2026, 16:40
  14. 15. Variance reduction with Normalizing Flows - Application to NNLO QCD phase space integration
    Rene Poncelet (IFJ PAN Krakow)
    06/01/2026, 09:00
  15. 16. Learning reliable amplitude surrogates
    Nina Elmer (University of Cambridge)
    06/01/2026, 09:40
  16. 17. Coffee break
    06/01/2026, 10:20
  17. 18. Neural Network Tools for Amplitudes
    Mr Daniel Pierre Maitre
    06/01/2026, 10:50
  18. 19. Quantum computation of perturbative QFTs
    Herschel Chawdhry (Florida State University)
    06/01/2026, 11:30
  19. 20. Lunch
    06/01/2026, 12:10
  20. 25. On Foundation models for Fundamental Science [Title to be confirmed]
    Michael Kagan (SLAC National Accelerator Laboratory (US))
    06/01/2026, 13:30
  21. 22. OmniLearn: A Method to Simultaneously Facilitate All Jet Physics Tasks and beyond
    Vinicius Massami Mikuni (Lawrence Berkeley National Lab. (US))
    06/01/2026, 14:10
  22. 24. AI for Discovery
    Gregor Kasieczka (Hamburg University (DE))
    06/01/2026, 14:50
  23. 23. Coffee break
    06/01/2026, 15:30
  24. 21. Uncertainty Quantification in PDF Determinations
    Luigi Del Debbio (The University of Edinburgh (GB))
    06/01/2026, 16:00
  25. 27. Unfolding, Precision, and Machine Learning
    Kyle Cormier (CERN)
    06/01/2026, 16:40
  26. 38. Dinner
    06/01/2026, 18:00

    Restaurant Zum Alten Löwen, Universitätstrasse 111, 8006 Zürich.
    Directions: Directions on Google Maps
    Bus line 33 or tram lines no. 9 & 10,
    Rigiblick cable car stop.

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  27. 11. Can AI Enable Next Breakthroughs in Large-Scale Structure Cosmology?
    Tomasz Kacprzak
    07/01/2026, 09:00
  28. 12. Simulation-based inference for gravitational-wave data analysis
    Konstantin Leyde
    07/01/2026, 09:40
  29. 29. Coffee break
    07/01/2026, 10:20
  30. 31. On Detector Design with Differential Methods
    Jan Kieseler (KIT - Karlsruhe Institute of Technology (DE))
    07/01/2026, 10:50
  31. 30. On Detector Design with Artificial Intelligence
    Shah Rukh Qasim (University of Zurich (CH))
    07/01/2026, 11:30
  32. 32. Lunch
    07/01/2026, 12:10
  33. 35. Closing remarks
  34. 13. TBA
  35. 33. TBA
  36. 34. TBA
  37. 36. TBA
  38. 37. TBA