AstroAI Workshop

Abstract

Many machine learning and data science applications involve data with geometric structure, such as graphs, strings, and matrices, or data with symmetries that arise from fundamental laws of physics in the underlying system. In this talk we discuss how we can identify such structure in data and models using geometric tools, as well as examples of leveraging such structure for the design of more efficient machine learning algorithms. We will also discuss applications of such methods in the Sciences.