Introduction

• Cristiano Fanelli (MIT)
• Tanja Horn (Cath)

Uncertainty aware ML-based models for accelerator studies (Anomaly detection and surrogate model for RL)

• Kishan Rajput (Jefferson Lab)

Uncertainty Quantification for Machine Learning Applied to Data Analysis

• Benjamin Nachman (Lawrence Berkeley National Laboratory)

Inverse problems in nuclear tomography

• Nobuo Sato (Jefferson Lab)

Final Discussion