This page gathers materials and documentation highlighting community-driven efforts to promote and advance the use of Artificial Intelligence and Machine Learning for the Electron-Ion Collider. Here you'll find a curated collection of project repositories, tutorials, lectures and hackathons — all aimed at sharing knowledge, building skills, and fostering collaboration across the AI4EIC community. Whether you're a newcomer or an experienced contributor, this hub is your starting point to explore, learn, and get involved.

AI4EIC RESOURCE HUB

Lectures/Tutorials

Below are provided links to lectures, tutorials on AI/ML applications useful for the EIC detector.

Data Science for Physicists (APS GPS 2026)

APS GPS 2026

https://ai4eic.github.io/APS2026_GPS_tutorials/landing_page.html

GNN for FCAL (classification and regression)

Dataset and models (HuggingFace) - Links: [1], [2]

Deep Learning Tutorials for Experimental Nuclear Physics (APS DNP 2025)

APS DNP 2025

https://ai4eic.github.io/DNP2025-tutorials/landing_page.html

CNN for FCAL (classification and regression)
Generative AI for FCAL

Dataset and models (HuggingFace) - Links: [1], [2]

AI/ML for the Electron Ion Collider

HUGS GRADUATE PROGRAM

A mini-series of lectures on AI/ML for Nuclear Physics and the Electron Ion Collider, taught at HUGS2023, HUGS2025.

https://cfteach.github.io/HUGS2025

https://cfteach.github.io/HUGS23/

Continual Learning, A. Cossu (U. of Pisa)

CONTINUAL LEARNING

Link to presentation and references therein to code repositories

https://indico.bnl.gov/event/19560/contributions/82545/attachments/52139/89172/AI4_EIC.pdf

Reinforcement Learning, H. Chen (W&M)

REINFORCEMENT LEARNING

Link to presentation and references therein to code repositories

https://indico.bnl.gov/event/19560/contributions/83355/attachments/51393/87879/RL-tutorial-AIEIC.pdf

Detector Design with AI, C. Fanelli (W&M)

DESIGN

Link to presentation and references therein to code repositories An interactive Jupyter book presented at the NNPSS Summer School at MIT, which includes lectures and hands-on tutorials on AI-assisted design with a fully documented description of the optimization adopted during the EIC detector proposal.

https://cfteach.github.io/nnpss

https://indico.bnl.gov/event/19560/contributions/83355/attachments/51393/87879/RL-tutorial-AIEIC.pdf

Multi-Objective Optimization (Ax, BoTorch), M. Balandat (Meta/AI)

MULTI-OBJECTIVE OPTIMIZATION

Link1

Link2

Unfolding with ML : OmniFold, F. Torales Acosta (LBNL), V. Mikuni (NERSC)

UNFOLDING - OMNIFOLD

GitHub link here: https://github.com/ftoralesacosta/AI4EIC_Omnfold
Colab link: https://colab.research.google.com/drive/1zuU9MezTIQGPhXlPG1Y9QilyDcQk6L0K?usp=sharing
Specifically, the notebook: https://github.com/ftoralesacosta/AI4EIC_Omnfold/blob/master/DIS_Omnifold.ipynb
Two data files on google drive that the tutorial uses:
- https://drive.google.com/file/d/1aqxnY0qxTrNZzijoLIUD0StEwmEEVJW7/view?usp=sharing
- https://drive.google.com/file/d/1qaeH6Z1xjAzCDuII8DQyDs42F_Ow2rkX/view?usp=sharing

Machine Learning Lifecycle, K. Rajput (Jefferson Lab/Data science)

ML LIFECYCLE

Colab link: https://colab.research.google.com/drive/1qPIyfefaqofX1wNQ3TYPT_ABy749Ohd2?usp=sharing

Graph Neural Networks, Y. (Ray) Ren

GRAPH NEURAL NETWORKS

Colab link: https://colab.research.google.com/drive/16fF6q1CSnxnEqRSl7LDAb0evscfqMOrf?usp=sharing

Artificial Intelligence for the Electron Ion Collider

Artificial Intelligence for the Electron Ion Collider

AI4EIC RESOURCE HUB

Project Repositories

https://github.com/ai4eic

Lectures/Tutorials

Data Science for Physicists (APS GPS 2026)

Deep Learning Tutorials for Experimental Nuclear Physics (APS DNP 2025)

AI/ML for the Electron Ion Collider

Continual Learning, A. Cossu (U. of Pisa)

Reinforcement Learning, H. Chen (W&M)

Detector Design with AI, C. Fanelli (W&M)

Multi-Objective Optimization (Ax, BoTorch), M. Balandat (Meta/AI)

Unfolding with ML : OmniFold, F. Torales Acosta (LBNL), V. Mikuni (NERSC)

Machine Learning Lifecycle, K. Rajput (Jefferson Lab/Data science)

Graph Neural Networks, Y. (Ray) Ren

Artificial Intelligence for the Electron Ion Collider

AI4EIC RESOURCE HUB

Project Repositories

https://github.com/ai4eic

Lectures/Tutorials

Data Science for Physicists (APS GPS 2026)

Deep Learning Tutorials for Experimental Nuclear Physics (APS DNP 2025)

AI/ML for the Electron Ion Collider

Continual Learning, A. Cossu (U. of Pisa)

Reinforcement Learning, H. Chen (W&M)

Detector Design with AI, C. Fanelli (W&M)

Multi-Objective Optimization (Ax, BoTorch), M. Balandat (Meta/AI)

Unfolding with ML : OmniFold, F. Torales Acosta (LBNL), V. Mikuni (NERSC)

Machine Learning Lifecycle, K. Rajput (Jefferson Lab/Data science)

Graph Neural Networks, Y. (Ray) Ren

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