7th Inter-experiment Machine Learning Workshop

This is the seventh annual workshop of the LPCC inter-experimental machine learning working group.

The workshop will be held on 19May-23May 2025 at CERN in a hybrid format, with remote participation made possible.

Confirmed invited speakers

• Aishik Gosh (UC Irvine)

• Patrick Kidger (Cradle.bio)

• Claudius Krause (Vienna, ÖAW)

• Ilaria Luise (CERN)

• Enrique Rico Ortega (CERN)

• Ricardo Rocha (CERN)

• Eric Wullf (CERN)

Warning

If you receive any email by a "Global Travel Experts" company or any other similar company requesting your itinerary or other personal information or promising accommodation, please be aware it is a scam, and please report it to the CERN IT department https://information-technology.web.cern.ch/. 

Workshop format

Following the success of the last edition format, the structure of the workshop in terms of contributions is focused on poster presentations. The reason behind this approach is to be able to allocate a large number of contributions while promoting a strong interaction between the presenters/participants. For this reasons, we require the poster presenters to attend in person. A small number of contributed submissions will be selected for oral presentations.

You will have to arrange for your own accommodation, either in the CERN Hostel (https://edh.cern.ch/Hostel/, subject to room availability) or in nearby hotels.

Please make sure to be registered to lhc-machinelearning-wg@cern.ch CERN egroup, to be informed of any unforeseen circumstance.

The preliminary structure of the workshop includes:

• Tutorials 
• Plenary invited talks from academy
• Plenary invited talks from industry
• Poster sessions
• Plenary contributed talks

Workshop tracks

For the contributed posters and potential talks, the following Tracks have been defined:

1. ML for object identification and reconstruction
2. ML for analysis: Event classification, statistical analysis and inference, anomaly detection
3. ML for simulation and surrogate model: Application of ML for simulation or cases of replacing an existing complex model
4. LLMs and foundation models
5. Fast ML: Application of ML to DAQ/Trigger/Real Time Analysis/Edge Computing
6. ML infrastructure: Hardware and software for ML/MLOps
7. ML training, courses, tutorials, open datasets and challenges
8. ML in astroparticle physics
9. ML in phenomenology and theory
10. ML for particle accelerators
11. Other

IML group

This workshop is organized by the CERN IML coordinators. To keep up to date with ML at LHC, please register to lhc-machinelearning-wg@cern.ch CERN egroup.