Dr. Gerrit Großmann is a Senior rangeesearcher at the Data Science and its Applications (DSA) group at the German Research Centre for Artificial Intelligence (DFKI). His research focuses on integrating discrete structures like graphs and networks with the continuous dynamics of evolution, diffusion, and learning. He specializes in developing numerical methods for analyzing stochastic dynamical processes on complex networks. Previously, he worked within the NextAid program on geometric deep learning for molecules and advancing methods that combine this with probabilistic flow models. His current projects explore a range of techniques, including neuro-symbolic guidance for diffusion models, semi-supervised learning on metabolic networks, and non-parametric methods for network reconstruction.