research

AI is already reshaping how we work, learn, and make decisions. We feel a responsibility to guide and uncover how machine learning can foster economically feasible solutioons that best address long-term human needs. We spin off new projects like robotics for sustainable agriculture or individualised learning, engage in mentoring and foster an open-minded innovation- and impact-driven mindset.

We investigate the similarities and differences between human and machine perception, learning, and reasoning. By comparing how humans and artificial systems see, understand, and make sense of the world, we aim to identify key gaps that limit machine intelligence and uncover principles that drive human cognition. Our work spans vision, language, and general reasoning, combining behavioral experiments, computational modeling, and machine learning. These insights guide the development of AI systems that are more adaptable, robust, and aligned with human capabilities. We collaborate with Felix Wichmann and Matthias Bethge.

We study how to align perception, cognition, and action so that AI systems can build and use internal world models for multi-modal reasoning. Our work connects signals across vision, language, and interaction, then tests whether the resulting representations support explanation, counterfactual inference, and planning. The goal is robust, human-aligned agents that can navigate complex environments, tasks, and decisions just like humans.

We study when and why learning systems generalize beyond their training data. Using identifiability theory, we characterize the conditions under which models can recover the underlying rules and structure from data—yielding representations and internal mechanisms that transfer to novel scenarios. Our goal is a rigorous foundation for trustworthy generalization, informing both theory and the design of models that reliably handle out-of-distribution cases.

We open the black box of modern neural networks by studying how information is represented, transformed, and routed inside them. Our work evaluates where popular visualisation methods (e.g., saliency, feature visualisation) succeed or fail, and develop tools and benchmarks that tie internal mechanisms to model behaviour. The goal is explanations that are reliable, actionable, and predictive—supporting debugging, safety, and the design of models that make decisions we can understand and trust.