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EuCAIFCon 2026

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

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Contribution List

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  1. 9. Welcome
    24/08/2026, 13:45
  2. 63. Jan Kieseler (KIT)
    Jan Kieseler (KIT)
    24/08/2026, 14:00
  3. 64. Mario Krenn
    Mario Krenn (University of Tübingen)
    24/08/2026, 14:45
  4. 65. Ramon Winterhalder
    Ramon Winterhalder (University of Milan)
    25/08/2026, 09:00
  5. 66. Lucie Flek
    Lucie Flek (University of Bonn)
    25/08/2026, 09:45
  6. 67. Dario Hügel
    Dario Hügel (Barra Labs)
    25/08/2026, 14:00
  7. 68. Nicole Hartman
    Nicole Hartman (TUM)
    25/08/2026, 14:45
  8. 69. Adrian Oeftiger
    Adrian Oeftiger (Linacre College)
    25/08/2026, 16:00
  9. 70. TBD
    25/08/2026, 16:45
  10. 71. Luigi DelDebbio
    Luigi DelDebbio (University of Edinburgh)
    26/08/2026, 09:00
  11. 72. Maximilian Dax
    Maximilian Dax (ELLIS Institute Tübingen)
    26/08/2026, 09:45
  12. 73. TBD
    26/08/2026, 11:00
  13. 74. Johann Brehmer
    Johann Brehmer (CuspAI)
    26/08/2026, 11:45
  14. 75. Emille Ishida
    Emille Ishida (Clermont Auvergne)
    27/08/2026, 09:00
  15. 76. Tristan Bereau
    Tristan Bereau (University of Heidelberg)
    27/08/2026, 09:45
  16. 77. Michelle Kuchera
    Michelle Kuchera (Florida State University)
    27/08/2026, 16:30
  17. 78. Roberto Trotta
    Roberto Trotta (SISSA)
    27/08/2026, 17:15
  18. 12. Closing words
    28/08/2026, 12:00
  19. 39. ARQuAdia-Net: Learning Quantum Annealing Dynamics for 3-SAT
    Lorenzo Colantonio (Sapienza Università di Roma and INFN)

    In recent years, approaches inspired by fundamental physics have played an important role in the development of modern machine learning algorithms, from energy based models to diffusion processes. Motivated by this perspective, we propose a hybrid quantum-classical learning framework inspired by adiabatic quantum dynamics and quantum annealing for solving constraint satisfaction problems.In...

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  20. 23. Building foundation models with low-level collision data in particle physics
    Shreya Saha (Adelaide University)
    Foundation Models
    Foundation Models

    The integration of foundation models in particle physics is gaining pace rapidly and has expanded the search for new physics. This talk presents foundation models trained on low-level data from the first fully simulated dataset using Open Data Detector (ColliderML), to distinguish between Standard Model and Beyond Standard Model processes. We compare new physics discovery using only low level...

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  21. 42. Community Detection on Complex Networks through Transformer-based Message Passing and Denoising Diffusion
    Edoardo Murano (Sapienza Università di Roma and INFN)

    Partitioning a graph into communities is an NP-hard optimization problem with a natural statistical-physics formulation: the optimal partition is the ground state of an antiferromagnetic Potts model, with modularity playing the role of negative energy. Clustering on graph-structured data is a recurring task in fundamental physics, where detector and event data are naturally represented as...

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  22. 44. Differentiable simulation and optimization of superconducting quantum sensors
    Nathan Campioni (Sapienza Università di Roma and INFN)

    QSOpt (Quantum Sensing Optimization) is an end-to-end differentiable simulation and machine-learning optimization framework for open quantum networks composed of superconducting qubits, bosonic modes and input-output channels with user-defined interactions.
    The advancements in quantum technologies have sparked interest in employing quantum systems as sensors, with superconducting quantum...

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  23. 30. Diffusion Models for SU(2) Lattice Gauge Theory in Two Dimensions
    Bao-Dong Sun (Ruhr-Universität Bochum)
    Simulations & Generative Models

    We apply score-based diffusion models to two-dimensional SU(2) lattice pure gauge theory with the Wilson action, extending recent work on U(1) gauge theories. The SU(2) manifold structure is handled through a quaternion parameterization. The model is trained on 10,000 configurations generated via Hybrid Monte Carlo at a fixed coupling $\beta_0= 2.0$ on an $8\times 8$ lattice, augmented to...

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  24. 57. Estimating Neutrino Oscillation Parameters using Structured hierarchical transformers
    Antonin Vacheret (CNRS - LPC Caen)

    Neutrino oscillations encode fundamental information about neutrino masses and mixing parameters, offering a unique window into physics beyond the Standard Model. Estimating these parameters from oscillation probability maps is, however, computationally challenging due to the maps’ high dimensionality and nonlinear dependence on the underlying physics. Traditional inference methods, such as...

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  25. 38. Gauge equivariant transformer for lattice gauge theories
    Fracesco Passante (Sapienza Università di Roma and INFN)

    Gauge equivariant convolutional neural networks have shown that enforcing exact lattice gauge symmetry in a neural network significantly improves regression accuracy on gauge invariant observables. However, their convolutional kernels stay fixed after training and cannot adapt to configuration-dependent features. We introduce GELT (Gauge Equivariant Lattice Transformer), a gauge equivariant...

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  26. 59. Hybrid GNN-Bayesian Models for Uncertainty-Aware Inference of Rare Events on Spatially Embedded Graphs
    Federica Di Bartolomeo (Sapienza Università di Roma)

    We present a hybrid graph-based Bayesian framework for uncertainty-aware inference of rare events on spatially embedded networks.
    Starting from a Multivariate Conditional Autoregressive Model (MCAR) in a hierarchical Bayesian architecture defined on the dual graph of a street network, events counts are described by Poisson likelihoods with structured graph effects, unstructured heterogeneity...

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  27. 53. JetParticle-JEPA: An Efficient Self-Supervised Representation Learning method for Jet Tagging in High-Energy Physics
    Guillaume Letellier (GREYC - University of Caen Normandy, France)
    Foundation Models
    Foundation Models

    Jet tagging at the Large Hadron Collider (LHC) increasingly relies on deep learning models trained on massive simulated datasets, leading to high computational costs and limited robustness to detector mismodeling. We introduce JetParticle-JEPA (JP-JEPA), a self-supervised Joint-Embedding Predictive Architecture that learns physically meaningful jet representations directly from continuous...

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  28. 10. Plenary
  29. 11. Plenary
  30. 8. Reconstruction of Gravitational Form Factors using Generative Machine Learning
    Iuliia Panteleeva
    Simulations & Generative Models
    Simulations & Generative Models

    Extracting hadronic form factors from sparse and noisy lattice QCD data typically relies on parametric ansätze, introducing model-dependence. We present a generative framework based on denoising diffusion models for parameterisation-independent reconstruction of these quantities. The generative prior is built from a large ensemble of synthetic curves drawn from distinct functional classes...

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  31. 40. Variational Positive-Unlabeled Learning with Observational Bias: Uncertainty-Aware Discovery in Incomplete Binary Data
    Gabriele Pignalberi (Sapienza Università di Roma and INFN)

    Many scientific datasets are fundamentally incomplete: only a biased subset of true positives is ever observed, while the remainder stay unlabeled. This Positive-Unlabeled (PU) setting arises whenever detection efficiency is imperfect and covariate-dependent—a structure shared across many fields in fundamental science. In hadron collider experiments, hardware triggers record only a biased...

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