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EuCAIFCon 2026

24–28 Aug 2026
Kirchhoff Institute for Physics (KIP)
Europe/Berlin timezone

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Session

🗣️ Plenaries

24 Aug 2026, 14:00
HS1

HS1

Conveners

🗣️ Plenaries

  • Tilman Plehn (Institut für Theoretische Physik, Universität Heidelberg; Interdisciplinary Center for Scientific Computing (IWR), Universität Heidelberg)

🗣️ Plenaries

  • Gabrijela Zaharijaš (University of Nova Gorica)

🗣️ Plenaries

  • Kai Zhou (CUHK-Shenzhen)

🗣️ Plenaries

  • Caterina Doglioni (University of Manchester)

🗣️ Plenaries

  • Johan Messchendorp (GSI GmbH/Ruhr Universität Bochum)

🗣️ Plenaries

  • Martin Erdmann

🗣️ Plenaries

  • Verena Kain

🗣️ Plenaries

  • Caterina Doglioni (University of Manchester)

Presentation materials

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  1. 63. From Classifiers to Detectors: Learning Closer to the Data
    Jan Kieseler (KIT)
    24/08/2026, 14:00
  2. 64. Towards an Artificial Muse for new Ideas in Science
    Mario Krenn (University of Tübingen)
    24/08/2026, 14:45
  3. 65. Theory ex Machina: Machine Learning the LHC Simulation Chain
    Ramon Winterhalder (University of Milan)
    25/08/2026, 09:00
  4. 66. The Model Says Yes. Now What? And Other Adventures in Scientific AI
    Lucie Flek (University of Bonn)
    25/08/2026, 09:45

    What can we actually conclude when a model gives us the right answer? Did it learn the right correlations? Does it generalize beyond the setting in which we tested it? And what does it mean to validate increasingly complex "scientific AI" models and workflows? I will explore these questions through examples and cautionary tales from language modeling, human modeling, particle physics and...

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  5. 67. Architectural Challenges in Enterprise Agentic AI
    Dario Hügel (Barra Labs)
    25/08/2026, 14:00

    At barra, we implement agentic AI projects within complex, large-scale enterprise environments. The enterprise landscape introduces significant organisational complexity as well as rigid requirements for stability, scalability, and economic viability. Navigating these requirements demands a departure from experimental setups. This talk provides an overview of our operational learnings,...

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  6. 68. Foundation Models and Scale: Deep Networks unlock deep mysteries
    Nicole Hartman (TUM)
    25/08/2026, 14:45
  7. 69. Particle Accelerators, Beam Instabilities and Data-driven Modelling
    Adrian Oeftiger (University of Oxford, John Adams Institute)
    25/08/2026, 16:00
  8. 70. Machine Learning for the SKA
    Caroline Heneka
    25/08/2026, 16:45
  9. 71. The Mechanics of Learning: Inverse Problems
    Luigi DelDebbio (University of Edinburgh)
    26/08/2026, 09:00

    ML techniques are routinely used to solve Inverse Problems in particle physics. Focussing on the case of PDF fitting as a testbed, we develop a description of the training dynamics during gradient descent. Our analysis shows that the ML solution is one way of regulating an otherwise ill-defined problem and allows us to address the role of hyperparameters in the training process. A critical...

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  10. 72. AI for Gravitational-Wave Data Analysis
    Maximilian Dax (ELLIS Institute Tübingen)
    26/08/2026, 09:45
  11. 73. Fast Particle Detector Simulation and Reconstruction
    Eilam Gross
    26/08/2026, 11:00
  12. 74. Physics as inductive bias: Free energies and changes of resolution
    Tristan Bereau (University of Heidelberg)
    26/08/2026, 11:45
  13. 75. Light in the dark: How machine learning changed observational astronomy
    Emille Ishida (Clermont Auvergne)
    27/08/2026, 09:00
  14. 76. On the cusp of on-demand material discovery
    Johann Brehmer (CuspAI)
    27/08/2026, 09:45
  15. 77. TPC Foundation Models for Cross-Experiment and Cross-Detector Transfer
    Michelle Kuchera (Florida State University)
    27/08/2026, 16:30
  16. 78. Simulation-Based Inference: Promises and Challenges
    Roberto Trotta (SISSA)
    27/08/2026, 17:15
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