AI is currently the central theme in science. Whereas the underlying algorithms rely on rather simple mathematical operations such as matrix-vector multiplications and applying non-linearities componentwise, deriving a theoretical understanding proves to be extremely challenging. To identify synergies between the fields of mathematical statistics and theoretical machine learning, the workshop brought together leading researchers and rising stars who are tackling core challenges at the intersection of these fields. We have identified the topics of robustness and model misspecification, statistical theory for neural networks and statistics for stochastic processes as three key themes that underpin increasingly many current developments. These topics were the focus of the talks and research that was carried out during the Oberwolfach week.