Andreas Schlaginhaufen received his Bachelor's and Master's degrees in Electrical Engineering and Information Technology from ETH Zurich, including an exchange semester at the University of Toronto, Canada. He interned with EWZ Zurich's power grid development team and ETH Zurich's Learning & Adaptive Systems group. His master's thesis focused on stable deep dynamics models for partially observed or time-delayed systems. His PhD research focuses on the theoretical and practical aspects of aligning reinforcement learning models with human preferences and expert demonstrations. His broader interests include safe reinforcement learning, convex analysis, and learning in games.