Speaker
Alexander Held
(University of Wisconsin Madison (US))
Description
The field of high energy physics (HEP) benefits immensely from sophisticated simulators and data-driven techniques to perform measurements of nature at increasingly higher precision. Using the example of HEP, I will describe how and where uncertainties are incorporated into data analysis to address model misspecification concerns. My focus will be how machine learning (ML), in the variety of ways it is employed in practice, affects considerations around mis-modeling.
