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ZPW2026: Machine Learning Techniques for Particle Physics

→ Europe/Zurich
KOL-G-217 (UZH Main Building)

KOL-G-217

UZH Main Building

Raemistrasse 71, 8006 Zurich
Description

The Zurich Phenomenology Workshop has been organized for the first time by ETH and the University of Zurich in 2009. Over the years, the Workshop has developed into a forum for particle physics researchers to discuss the latest developments in phenomenology. 

In 2026 we will have a focus on rapidly evolving role of machine learning in high energy physics. We will cover a range of topics from phenomenology to detector design. The ZPW 2026 will provide a forum to discuss emerging opportunities where machine learning can advance precision calculation, beyond the Standard Model physics and discovery potential across the field.

The workshop, organized jointly by the particle theory groups of UZH and ETH with the support of the Pauli Center for Theoretical Studies, will be held on January 5-7, 2026.

 

Registration is now closed. 

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Participants
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  • Monday 5 January
    • 08:30 → 09:30
      Registration 1h KOL-G-217

      KOL-G-217

      UZH Main Building

      Raemistrasse 71, 8006 Zurich
    • 09:30 → 09:35
      Welcome 5m KOL-G-217

      KOL-G-217

      UZH Main Building

      Raemistrasse 71, 8006 Zurich
    • 09:35 → 10:55
      Session 1: Deep learning and neutrino physics KOL-G-217

      KOL-G-217

      UZH Main Building

      Raemistrasse 71, 8006 Zurich
      Convener: Florencia Canelli (University of Zurich (CH))
      • 09:35
        Experimental View on Deep Learning for Neutrino experiments 30m
        Speaker: Davide Sgalaberna (ETH Zurich (CH))
        ML for neutrinos.pdf
      • 10:15
        Algorithmic View on Deep Learning for Accelerator Neutrino Experiments 30m
        Speaker: Dr Saul Alonso Monsalve (ETH Zurich)
        zpw-sam-26-01-05.pdf
    • 10:55 → 11:25
      Coffee break 30m KOL-G-217

      KOL-G-217

      UZH Main Building

      Raemistrasse 71, 8006 Zurich
    • 11:25 → 12:45
      Session 2: Machine Learning and Lattice Field Theory KOL-G-217

      KOL-G-217

      UZH Main Building

      Raemistrasse 71, 8006 Zurich
      Convener: Marina Krstic Marinkovic (ETH Zurich)
    • 12:45 → 14:10
      Lunch 1h 25m KOL-G-217

      KOL-G-217

      UZH Main Building

      Raemistrasse 71, 8006 Zurich
    • 14:10 → 15:30
      Session 3: Optimal transport, anomaly detection KOL-G-217

      KOL-G-217

      UZH Main Building

      Raemistrasse 71, 8006 Zurich
      Convener: Nicola Serra (University of Zurich (CH))
      • 14:20
        Optimal Transport: from A to B...and beyond 30m
        Speaker: Prof. Alessio Figalli (ETH)
        Figalli-2026.pdf
      • 15:00
        Data to theory: how to fuel BSM model building with anomaly detection 30m
        Speaker: Maurizio Pierini (CERN)
        ZPW2026.pdf
    • 15:30 → 16:00
      Coffee break 30m KOL-G-217

      KOL-G-217

      UZH Main Building

      Raemistrasse 71, 8006 Zurich
    • 16:00 → 17:20
      Session 4: Machine learning for reconstruction KOL-G-217

      KOL-G-217

      UZH Main Building

      Raemistrasse 71, 8006 Zurich
      Convener: Thomas Gehrmann (Univ. Zurich)
  • Tuesday 6 January
    • 09:00 → 10:20
      Session 5: Machine Learning for NNLO QCD and Amplitude Surrogates KOL-G-217

      KOL-G-217

      UZH Main Building

      Raemistrasse 71, 8006 Zurich
      Convener: Thomas Gehrmann (Univ. Zurich)
      • 09:00
        Variance reduction with Normalizing Flows - Application to NNLO QCD phase space integration 30m
        Speaker: Rene Poncelet (IFJ PAN Krakow)
        2026.01.06-Zurich-ZPW-poncelet.pdf
      • 09:40
        Learning reliable amplitude surrogates 30m
        Speaker: Nina Elmer (University of Cambridge)
        ZPW26_Elmer.pdf
    • 10:20 → 10:50
      Coffee break 30m KOL-G-217

      KOL-G-217

      UZH Main Building

      Raemistrasse 71, 8006 Zurich
    • 10:50 → 12:10
      Session 6: Perturbative QCD and Matrix Elements KOL-G-217

      KOL-G-217

      UZH Main Building

      Raemistrasse 71, 8006 Zurich
      Convener: Gino Isidori (University of Zurich (CH))
    • 12:10 → 13:30
      Lunch 1h 20m KOL-G-217

      KOL-G-217

      UZH Main Building

      Raemistrasse 71, 8006 Zurich
    • 13:30 → 15:30
      Session 7: Machine Learning for Fundamental Physics and Discovery KOL-G-217

      KOL-G-217

      UZH Main Building

      Raemistrasse 71, 8006 Zurich
      Convener: Thea Aarrestad (ETH Zurich (CH))
      • 13:30
        On Foundation models for Fundamental Science [Title to be confirmed] 30m
        Speaker: Michael Kagan (SLAC National Accelerator Laboratory (US))
        Kagan_ZPW2026.pdf
      • 14:10
        OmniLearn: A Method to Simultaneously Facilitate All Jet Physics Tasks and beyond 30m
        Speaker: Vinicius Massami Mikuni (Lawrence Berkeley National Lab. (US))
        OmniLearned.pdf
      • 14:50
        AI for Discovery 30m
        Speaker: Gregor Kasieczka (Hamburg University (DE))
        DeepLearning_ZPW_2026.pdf
    • 15:30 → 16:00
      Coffee break 30m KOL-G-217

      KOL-G-217

      UZH Main Building

      Raemistrasse 71, 8006 Zurich
    • 16:00 → 17:20
      Session 8: Machine Learning for PDFs, Unfolding and Precision Physics KOL-G-217

      KOL-G-217

      UZH Main Building

      Raemistrasse 71, 8006 Zurich
      Convener: Javad Komijani (ETH Zurich)
      • 16:00
        Uncertainty Quantification in PDF Determinations 30m
        Speaker: Luigi Del Debbio (The University of Edinburgh (GB))
        Zurich26.pdf
      • 16:40
        Unfolding, Precision, and Machine Learning 30m
        Speaker: Kyle Cormier (CERN)
        Unfolding_Precision_ML_ZPW_2026.pdf
    • 18:00 → 21:00
      Dinner 3h

      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.

  • Wednesday 7 January
    • 09:00 → 10:20
      Session 9: Machine Learining in Cosmology KOL-G-217

      KOL-G-217

      UZH Main Building

      Raemistrasse 71, 8006 Zurich
      Convener: Marcelle Soares-Santos (University of Zürich)
      • 09:00
        Can AI Enable Next Breakthroughs in Large-Scale Structure Cosmology? 30m
        Speaker: Tomasz Kacprzak
        tomaszk_uzh_260107.pdf
      • 09:40
        Simulation-based inference for gravitational-wave data analysis 30m
        Speaker: Konstantin Leyde
        260107_zurich.pdf
    • 10:20 → 10:50
      Coffee break 30m KOL-G-217

      KOL-G-217

      UZH Main Building

      Raemistrasse 71, 8006 Zurich
    • 10:50 → 12:10
      Session 10: Machine Learning for Detector Design KOL-G-217

      KOL-G-217

      UZH Main Building

      Raemistrasse 71, 8006 Zurich
      Convener: Nicola Serra (University of Zurich (CH))
      • 10:50
        On Detector Design with Differential Methods 30m
        Speaker: Jan Kieseler (KIT - Karlsruhe Institute of Technology (DE))
        20260107.pdf
      • 11:30
        On Detector Design with Artificial Intelligence 30m
        Speaker: Shah Rukh Qasim (University of Zurich (CH))
        ZPW26_AI_for_Design.key
    • 12:10 → 13:40
      Lunch 1h 30m KOL-G-217

      KOL-G-217

      UZH Main Building

      Raemistrasse 71, 8006 Zurich
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