PyHEP 2025 - "Python in HEP" Users Workshop (hybrid), CERN

Session

Plenary Session Tuesday (3)

28 Oct 2025, 14:00

222/R-001 (CERN)

Conveners

Plenary Session Tuesday (3)

• Iason Krommydas (Rice University (US))

9. Up your ML game: an overview of Python ML tools relevant to HEP

Liv Helen Vage (Princeton University (US))

28/10/2025, 14:00

"Long talk"

Machine learning is advancing at a breathtaking pace, and navigating the ever-growing ecosystem of Python tools can be time consuming. This talk offers a practical guide to the ML landscape most relevant to high-energy physics. We discuss:

• Common ML frameworks including PyTorch, PyTorch Lightning, Keras, Jax, Scikit-learn - strengths and weaknesses and how to choose
• **ML...

28. Zero-overhead ML training from Python with ROOT in an ATLAS Open Data analysis

Martin Foll (University of Oslo (NO))

28/10/2025, 14:50

Lightning talk

The ROOT software framework is widely used from Python in HEP for storage, processing, analysis and visualization of large datasets. With the large increase in usage of ML from the Python ecosystem for experiment workflows, especially lately in the last steps of the analysis pipeline, the matter of exposing ROOT data ergonomically to ML models becomes ever more pressing. In this contribution...

14. From ROOT to PyTorch: Seamless Data Pipelines with HDF5

Jan Gavranovic (Jozef Stefan Institute (SI))

28/10/2025, 15:00

Lightning talk

High Energy Physics analyses frequently rely on large-scale datasets stored in ROOT format, while modern machine learning workflows are increasingly built around PyTorch and its data pipeline abstractions. This disconnect between domain-specific storage and general-purpose ML frameworks creates a barrier to efficient end-to-end workflows.

We introduce F9columnar...

5. Efficient Statistical Modeling for Particle Physics Using Computational Graphs in Python

Dr Giordon Holtsberg Stark (University of California,Santa Cruz (US))

28/10/2025, 15:30

"Standard talk"

Statistical modeling is central to discovery in particle physics, yet the tools commonly used to define, share, and evaluate these models are often complex, fragmented, or tightly coupled to legacy systems. In parallel, the scientific Python community has developed a variety of statistical modeling tools that have been widely adopted for their performance and ease of use, but remain...