Higgs Boson
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
Vector Boson Scattering
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
Computing
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
Neutrino Astrophysics
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
Electronics
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
WZ Production
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
b-Hadron Lifetimes
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