This talk will describe various ways of using structured machine learning models for predicting complex physical dynamics, generating realistic objects, and constructing physical scenes. The key insight is that many systems can be represented as graphs with nodes connected by edges, which can be processed by graph neural networks and transformer-based models. The goal of the talk is to show how structured approaches are making advances in solving increasingly challenging problems in engineering, graphics, and everyday interactions with the world.

Bio: Peter Battaglia is a research scientist at DeepMind. He earned his PhD in Psychology at the University of Minnesota, and was later a postdoc and research scientist in MIT's Department of Brain and Cognitive Sciences. His current work focuses on approaches for reasoning about and interacting with complex systems, by combining richly structured knowledge with flexible learning algorithms.