Gerrit Großmann

Gerrit Großmann is a researcher and educator at the Universität des Saarlandes, focusing on numerical methods for stochastic dynamical processes on complex networks. His work bridges computational epidemiology, network science, and machine learning, with applications to epidemics, drug discovery, and AI-driven scientific discovery. He has developed tools like the Network Epidemic Playground and contributed to open-source platforms such as TeachOpenCADD.

Education includes a PhD in Stochastic Spreading on Complex Networks, a Master's in Lumping the Approximate Master Equation, and a Bachelor's in Markov Model Likelihoods. His research emphasizes the limitations of traditional ODE models in epidemiology and advocates for more nuanced stochastic approaches.

Teaching spans advanced topics in diffusion modeling, AI for drug design, and complex network dynamics. Current interests include LLM4Science, exploring how large language models can transform scientific reasoning and collaboration.

Key contributions include analyzing the role of network structure in epidemic spread, developing efficient simulation techniques, and critiquing predictive modeling practices in public health. His tools and frameworks are widely used in academic and applied research settings.