Stefan Bauer: Neural causal models

Deep neural networks have achieved outstanding success in many tasks ranging from computer vision, to natural language processing, and robotics. However such models still pale in their ability to understand the world around us, as well as generalizing and adapting to new tasks or environments. One possible solution to this problem are causal models, since they can reason about the connections between causal variables and the effect of intervening on them. This talk will introduce the fundamental concepts of causal inference, connections and synergies with deep learning as well as practical applications and advances in sustainability and AI for science.

Stefan Bauer is an Associate Professor at TU Munich and a Senior PI at Helmholtz AI. Prior to his appointment in Munich, he was an Assistant Professor at KTH Stockholm and a Group Leader at the Max Planck Institute for Intelligent Systems in Tübingen. Stefan Bauer obtained his Ph.D. in Computer Science from ETH Zurich, for which he was honored with the ETH Medal for an outstanding dissertation. He previously studied Mathematics at ETH Zurich and Economics and Finance at the University of London. As an organizer of competitions for the application and development of AI, such as drug discovery or the real-robot-challenge.com, he advocates for the democratization and transparency of machine learning research.