Daniel Herbst

I am a first-year PhD student in ML at Technical University of Munich under the mentorship of Prof. Stefanie Jegelka. Before starting my PhD, I earned an MSc in Mathematics at TU Munich, where I worked with the DAML group on symmetries and long-range interactions in graph neural networks (GNNs) as a research assistant, and was advised by Stefanie Jegelka for my Master thesis on transferability of GNNs. During my MSc, I also completed various industry internships as well as an exchange at University of Waterloo. Prior to this, I obtained a BSc in Mathematics at Karlsruhe Institute of Technology.

My research centers on the theoretical foundations of machine learning with a focus on reliability and robustness in graph learning. I am passionate about developing theory-guided methods that improve generalization and extrapolation in real-world, safety-critical applications.

I am always open to collaborations and thesis/project supervisions (math or CS)—feel free to reach out!