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Artificial Intelligence for the Electron Ion Collider (AI4EIC) 2025

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

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Contribution List

62 / 62
75. Welcome and Introduction
Cristiano Fanelli (William & Mary), Marisa LaFleur (MIT)
10/27/25, 9:30 AM
26. Explainable and Differential Reinforcement Learning for Multi Objective Optimization in Particle Accelerators
Kishansingh Rajput (Jefferson Lab)
10/27/25, 10:00 AM
27. Differentiable beam dynamics codes, their use in AI-ML for accelerators and potential applications to the EIC
Chenran Xu (Argonne National Laboratory)
10/27/25, 10:25 AM
28. Symplectic machine learning model for fast simulation of space-charge effects
Jinyu Wan (Facility for Rare Isotopes, Michigan State University)
10/27/25, 10:50 AM
29. Use of AI/ML for higher brightness and higher polarization of hadron beams
Eiad Hamwi (Cornell University)
10/27/25, 11:30 AM

We report on the use of AI/ML techniques to advance the pursuit of higher brightness and polarization of hadron beams in the RHIC/EIC injector chain. Bayesian inference applied to individual magnet strengths reduced quadrupole field uncertainty by a factor of two while shifting mean values away from prior expectations, thereby improving the reliability of accelerator models. Bayesian...
30. Framework for the Development of Virtual Accelerator Models for Machine Learning Applications
Adwaith Ravichandran (Argonne National Laboratory)
10/27/25, 11:55 AM
31. Machine-Learning–Accelerated Bayesian Uncertainty Quantification for Digital Twin Modeling and Control of the AGS Booster
Christopher Kelly (Brookhaven National Laboratory)
10/27/25, 12:15 PM
32. Machine Learning Approaches to Improved Ion Profile Monitor Measurements
Christopher Hall (RadiaSoft LLC)
10/27/25, 12:35 PM
33. Use of Generative AI and LLMs for Accelerator Design
Onur Gilanliogullari (member@anl.gov)
10/27/25, 12:55 PM
69. Tree-distilled autoencoders on FPGA for anomaly detection and data compression
Tae Min Hong (University of Pittsburgh (US)), Tae Min Hong (University of Pittsburgh)
10/27/25, 2:30 PM
70. Compression by Importance and More
Yi Huang (Brookhaven national lab)
10/27/25, 2:55 PM

In this talk, we present our work on compressing Time Projection Chamber data by signal importance. We also discuss the challenge of achieving more flexible compression after the neural network is trained.
71. AI-Enabled Data Quality Monitoring with Hydra
Thomas Britton (JLAB)
10/27/25, 3:20 PM
72. SMOCS – JLab’s Streaming Monitoring Optimization Control System
Armen Kasparian (Jefferson Lab)
10/27/25, 4:00 PM
73. Real-time AI-based dead hot map in the ePIC detector: a self-adaptive alternative to traditional big data calibration pipelines
Balazs Ujvari (University of Debrecen)
10/27/25, 4:25 PM
74. Optimal Control of Polarized Sources and Targets
Patrick Moran (W&M)
10/27/25, 4:50 PM
87. RAG4EIC Tutorial
Karthik Suresh (member@wm.edu;employee@wm.edu;faculty@wm.edu;staff@wm.edu)
10/28/25, 9:00 AM
34. ePIC AI/ML Overview
Dmitrii Kalinkin (Brookhaven National Laboratory)
10/28/25, 10:00 AM
36. Toward Unified Deep Learning Models for Simulation and PID with Cherenkov Detectors: the hpDIRC case
Cristiano Fanelli (W&M)
10/28/25, 10:20 AM
37. Tools for Unbinned Unfolding
Ryan Milton (UCR)
10/28/25, 10:35 AM
39. Multi-FPGA distributed MLP NN model for data reduction in ePIC dRICH readout system
Cristian Rossi (INFN Sezione di Roma)
10/28/25, 10:50 AM
40. Scalable AI-assisted Workflow Management for EIC Detector Design Across Distributed Heterogeneous Resources with PanDA-iDDS
Wen Guan (BNL)
10/28/25, 11:20 AM
41. Machine Learning for the pfRICH Particle Identification subsystem
Bishoy Dongwi, Charles-Joseph Naïm (Stonybrook)
10/28/25, 11:35 AM

We present an overview of the proximity-focusing Ring Imaging Cherenkov (pfRICH) detector developed for the ePIC experiment at the Electron-Ion Collider (EIC). Designed for the backward pseudorapidity region (−3.5 ≲ η ≲ −1.5), the pfRICH enables at least 3σ separation of pions, kaons, and protons up to 7 GeV/c, which is crucial for Semi-Inclusive Deep Inelastic Scattering (SIDIS) studies. In...
42. RAG-inspired Open-source based Q&A system for scholarly articles in EIC
Tapasi Ghosh (Ramaiah University of Applied Sciences)
10/28/25, 11:50 AM
43. ESI-Fastlight: a Conditional Normalizing Flow for Fast pfRICH Hitmap Generation
Gabor Galgoczi (BNL)
10/28/25, 12:05 PM
44. ML for the hKLM at the 2nd Detector
Rowan Kelleher (student@umich.edu;member@umich.edu;employee@umich.edu;student@annarbor.umich.edu;member@annarbor.umich.edu;employee@annarbor.umich.edu)
10/28/25, 12:20 PM
45. SRO AI/ML Models for Meson Structure Function Extraction
Sandeep Shiraskar (Catholic University of America)
10/28/25, 12:30 PM
46. Discussion
10/28/25, 12:40 PM
55. Relativity Wasn’t in the Training Set
Miles Cranmer (University of Cambridge)
10/28/25, 2:30 PM
56. Symbolic Regression
Douglas Adams (University of Virginia)
10/28/25, 2:50 PM
57. Neural Net ensembles for Bayesian inference of PDFs
Maria Ubiali (University of Cambridge)
10/28/25, 3:10 PM
58. Artificial Intelligence in the EIC era at the BSM-PDF frontier
Tim Hobbs (Argonne National Laboratory)
10/28/25, 3:45 PM
59. ML-accelerated sampling for theory
Phiala Shanahan (MIT)
10/28/25, 4:05 PM
60. Generative AI for data analysis and preservation
Marco Battaglieri (Jefferson Lab)
10/28/25, 4:25 PM
62. What we talk about when we talk about gluon saturation
Peter Jacobs (Lawrence Berkeley National Laboratory)
10/28/25, 4:45 PM
61. DeepSub: Deep Image Reconstruction for Background Subtraction in Heavy-Ion Collisions
Umar Sohail Qureshi (Stanford University)
10/28/25, 5:20 PM
64. Deep Neural Networks for Extracting the 3D Structure of Nucleon at the EIC
Dr Ishara Fernando (University of Virginia)
10/28/25, 5:30 PM
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)
10/28/25, 5:40 PM
67. Neural Network Generalized Parton Distributions
Zaki Panjsheeri (member@virginia.edu;student@virginia.edu;alum@virginia.edu;employee@virginia.edu;staff@virginia.edu)
10/28/25, 5:50 PM
76. AuroraGPT: A Foundation Model for Science
Rajeev Thakur (Argonne National Laboratory)
10/29/25, 10:00 AM
77. Smart Pixel Project : AI for pixel readout
Lindsey Gray (Fermilab)
10/29/25, 10:20 AM
78. Fast ML for Science
Nhan Tran (Fermilab)
10/29/25, 10:40 AM
79. Geometric GNNs for Charged Particle Tracking
Ahmed Mohammed (member@jlab.org;employee@jlab.org)
10/29/25, 11:00 AM
80. Electron-Proton Scattering Event Generation using Structured Tokenization
Steven Goldenberg (Jefferson Lab)
10/29/25, 11:10 AM
81. FM4NPP: A Scaling Foundation Model for Nuclear and Particle Physics
Shuhang Li (Columbia University)
10/29/25, 11:20 AM
82. Examples of AI for Particle Physics
Geoffrey Fox (University of Virginia)
10/29/25, 11:45 AM

We give two examples of using the diffusion model for Physics. The first is in the LHC CaloChallenge. The second study is unfolding and surrogates for Jefferson Laboratory physics.
83. SAGIPS: A scalable Framework for SciDAC QuantOm
Daniel Lersch (Jefferson Lab)
10/29/25, 12:05 PM
84. LLMs for particle physics analysis: results from studies on a toy model
Deepak Samuel (Central University of Karnataka)
10/29/25, 12:25 PM
85. AI Reasoning for Theoretical Physics
Yurii Kvasiuk (University of Wisconsin)
10/29/25, 12:45 PM
86. Discussion
10/29/25, 1:05 PM
47. A Unified Vision of AI/ML at the Electron-Ion Collider: Infrastructure and Capabilities
Linh Nguyen (Brookhaven National Laboratory)
10/29/25, 2:45 PM
49. ML in Production at SNS Accelerator
Anant Raj (ORNL)
10/29/25, 3:10 PM
48. Developing Fast ML on FPGA for Particle Identification and Tracking
Denis Furletov (Brandeis University)
10/29/25, 3:35 PM
52. MCP Agents in PanDA
Paul Nilsson (BNL)
10/29/25, 4:15 PM
53. FastML on FPGA
Dr Ming Liu (Los Alamos)
10/29/25, 4:40 PM
54. Discussion
10/29/25, 5:05 PM
68. A Generative AI Approach to Unbiased TMD Extraction
Marco Zaccheddu (Università degli studi di Cagliari & INFN)
35. Accessing $x$-dependent generalized parton distribution from generative AI
Zhite Yu (Jefferson Lab)
51. ML methods for statistical data analysis
Rafael Coelho Lopes de Sa (University of Massachusetts)
38. Quantum Tomography for Collider Physics: From Data-Driven Foundations to AI-Enhanced Methods
Daniel Tapia Takaki (University of Kansas)
63. Quantum Tomography for Collider Physics: From Data-Driven Foundations to AI-Enhanced Methods
Daniel Takaki (Kansas University)
66. SAGIPS: A scalable Framework for SciDAC QuantOM
Daniel Lersch (Jefferson Lab)
50. Title to Come
Sergey Furletov (Jefferson Lab)
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