Exploiting symmetry in structured data is a powerful way to improve learning and generalization ability of AI systems, and extract more information, in applications from vision and NLP to robotics. This is exemplified by convolutional neural nets, which are an ubiquitous architecture. Recently, there has been a great deal of progress to develop improved equivariant and invariant learning architectures, as well as improved data augmentation methods. There has also been progress on the theoretical foundations of the area, from the perspectives of statistics and optimization. The notion of adding data via data augmentation also arises in problems such as adversarial robustness. This workshop will bring together leading researchers in the area to discuss the state of the art of the field. The activity is part of the Center for Foundations of Information Processing at Penn, supported by NSF TRIPODS.