Machine Learning is here to stay and is shaping the future of fundamental physics research. From optimal inference, over theory-inspired network architectures, to anomaly detection, representation learning and foundation models, a new generation of scientists is driving these exciting developments. This school aims to further strengthen technical expertise and foster new connections.
The 2025 IWR School on Machine Learning for Fundamental Physics is aimed at advanced PhD students specializing in scientific machine learning. We particularly encourage registrations from researchers with experience in scientific machine learning, as demonstrated by papers or preprints related to the topic of the school. The school takes place at the Interdisciplinary Center for Scientific Computing (IWR) at Heidelberg University September 15th-19th 2025.
Central themes of the school are:
- Modern network architectures
- Precision and uncertainties
- Scientific foundation models
- Generative Networks
- Representation learning
- Optimal inference
- Quantum field theory and networks
2025 lecturers:
- Thea Aarrestad (ETH Zurich) -- Particle experiments, anomalies
- Jim Halverson (Northeastern University) -- Particle theory, KANs
- Michael Kagan (SLAC) -- Representation learning, foundation models
- Siddharth Mishra-Sharma (Anthropic, Boston University) -- Cosmological analyses
- Veronica Sanz (University of Valencia) -- ML for data mining
- David Shih (Rutgers University) -- Linking particle and astrophysics
- Ramon Winterhalder (University of Milan) -- Generative Networks
Organizers:
Scientific: Tilman Plehn, David Shih, Caroline Heneka
At IWR: Jan Keese, Anne Leonhardt, Michael Winckler
Application
Application for this event is currently open.
