Speaker
Description
Modern collider experiments produce increasingly complex and information-rich data sets, calling for new computational strategies for their interpretation. In this talk, I will discuss recent developments in data analysis methods aimed at improving the extraction of physical information from collision events, with an emphasis on machine learning, quantum machine learning, and related statistical techniques. These approaches open new possibilities for precision studies, classification, anomaly detection, uncertainty-aware inference, and the efficient use of high-dimensional event information. I will highlight how such methods can complement more traditional analysis strategies and help address both practical and conceptual challenges in the interpretation of current and future collider data. The focus will be on general ideas and emerging directions that connect advanced computation with fundamental questions in particle physics.