At the 10th International Summer School on AI and Big Data, Dr. Gerrit Grossmann will talk about Molecule Generation with Graph Neural Networks and Probabilistic Diffusion.

Talk: Molecule Generation with Graph Neural Networks and Probabilistic Diffusion

Diffusion models are powerful tools for generating complex data across various domains. This talk will focus on applying these models to molecular graphs and 3D molecular conformations to enhance molecule design and drug discovery. We will explore the foundations of diffusion models, uncovering the underlying reasons for their effectiveness. Additionally, practical advice and insights will be provided for utilizing these models in real-world scenarios.

Bio

Gerrit Grossmann received his doctorate in Saarbrücken. His PhD topic was the behavior of stochastic processes on graphs and networks, including the spread of (online and offline) epidemics. He also worked within the interdisciplinary project NextAID, where he researched neuro-symbolic approaches for drug discovery, specifically by using diffusion models and graph neural networks.

Since 2023, Grossmann has been part of the research group for neuro-mechanistic modeling at DFKI in Saarbrücken. His research interests there revolve around the question of how to integrate the distinct realms of discrete structures such as graphs and networks with the continuous nature of dynamic evolution, diffusion, and learning.