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    Artificial Intelligence for the Electron Ion Collider (AI4EIC) 2025

    27–29 Oct 2025
    America/New_York timezone
    Artificial Intelligence for the Electron Ion Collider (2025)

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    Session

    AI/ML for Data Analysis and Theory

    28 Oct 2025, 14:30

    Presentation materials

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    1. 55. Relativity Wasn’t in the Training Set
      Miles Cranmer (University of Cambridge)
      28/10/2025, 14:30
    2. 56. Symbolic Regression
      Douglas Adams (University of Virginia)
      28/10/2025, 14:50
    3. 57. Neural Net ensembles for Bayesian inference of PDFs
      Maria Ubiali (University of Cambridge)
      28/10/2025, 15:10
    4. 58. Artificial Intelligence in the EIC era at the BSM-PDF frontier
      Tim Hobbs (Argonne National Laboratory)
      28/10/2025, 15:45
    5. 59. ML-accelerated sampling for theory
      Phiala Shanahan (MIT)
      28/10/2025, 16:05
    6. 60. Generative AI for data analysis and preservation
      Marco Battaglieri (Jefferson Lab)
      28/10/2025, 16:25
    7. 62. What we talk about when we talk about gluon saturation
      Peter Jacobs (Lawrence Berkeley National Laboratory)
      28/10/2025, 16:45
    8. 61. DeepSub: Deep Image Reconstruction for Background Subtraction in Heavy-Ion Collisions
      Umar Sohail Qureshi (Stanford University)
      28/10/2025, 17:20
    9. 64. Deep Neural Networks for Extracting the 3D Structure of Nucleon at the EIC
      Dr Ishara Fernando (University of Virginia)
      28/10/2025, 17:30
    10. 65. Extraction of Chiral Odd Compton form factors using Maximum Likelihood Method from Exclusive 𝛑0 production experiment.
      Dr Saraswati Pandey (member@virginia.edu;staff@virginia.edu;employee@virginia.edu)
      28/10/2025, 17:40
    11. 67. Neural Network Generalized Parton Distributions
      Zaki Panjsheeri (member@virginia.edu;student@virginia.edu;alum@virginia.edu;employee@virginia.edu;staff@virginia.edu)
      28/10/2025, 17:50
    12. 68. A Generative AI Approach to Unbiased TMD Extraction
      Marco Zaccheddu (Università degli studi di Cagliari & INFN)
    13. 63. Quantum Tomography for Collider Physics: From Data-Driven Foundations to AI-Enhanced Methods
      Daniel Takaki (Kansas University)
    14. 66. SAGIPS: A scalable Framework for SciDAC QuantOM
      Daniel Lersch (Jefferson Lab)
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