Robert Müller

After graduating in Mathematics at the Technical Univesity of Munich, Robert joined the ML department in Carnegie Mellon to work on generalization in reinforcement learning with information bottlenecks, then joined appliedAI to work on multimodal meta learning and batch reinforcement learning. In January 2021 he moved to Mila, where he conducts research in policy gradient methods.