Particle Physics
We develop modern machine learning methods to enable discovery in particle physics, with an emphasis on interpretability, robustness, and uncertainty quantification.
- Deep learning for jet physics
- Simulation-based inference
- Foundation models for HEP
AI for Scientific Discovery
Our work explores how artificial intelligence can accelerate discovery across the physical sciences by embedding structure, symmetries, and domain knowledge into learning algorithms.
- Physics-informed ML
- Representation learning
- Scientific foundation models
Quantum
We study theoretical and phenomenological aspects of physics beyond the Standard Model, with close ties to experimental searches.
- New physics signatures
- Effective field theories
- LHC phenomenology