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
Project Repositories
Below is provided a to the AI4EIC Organization where Repositories can be found
https://github.com/ai4eic
AI4EIC github
Ongoing projects:
- AI4EIC RAG-Summarization https://github.com/ai4eic/EIC-RAG-Project
- AI4EIC hackathons infrastructure https://github.com/ai4eic/AI4EICHackathon2023-Streamlit
Lectures/Tutorials
Below are provided links to lectures, tutorials on AI/ML applications for the EIC detector.
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
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
An interactive Jupiter 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.
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:
Machine Learning Lifecycle, K. Rajput (Jefferson Lab/Data science)
ML Lifecycle
MaGraph Neural Networks, Y. (Ray) Ren
Graph Neural Networks
AI/ML examples for the EIC
Contributors
This website uses cookies.
We use cookies to analyze website traffic and optimize your website experience. By accepting our use of cookies, your data will be aggregated with all other user data.