
Prof. Thomas Gärtner : Computational aspects of machine learning.

Maximilian Thiessen : Graph-based machine learning, active learning, and learning theory. Connects (graph) convexity theory with machine learning.

David Penz : Background in economics and theoretical computer science. Working on disentangled representations, knowledge transfer, and neuro-symbolic AI.

Tamara Drucks : Background in bioinformatics and theoretical computer science. Now interested in graphs, optimization, and (deep) learning theory.

Patrick Indri : Background in physics and data science. Interested in trustworthy machine learning focusing on privacy and robustness.

Fabian Jogl : Machine learning on graphs and especially graph neural networks. Combining ideas from algorithmics with machine learning.

Dr. Pascal Welke : Interested in similarity based learning on graphs, as well as counting of various substructures for fun and (machine learning) profit.

Dr. Sagar Malhotra : Machine learning, logic, and probability in all possible combinations.

Christoph Sandrock : Interested in active learning, graph learning, and learning theory.

Johannes Petersen : Algorithmic cheminformatics, graph algorithms, graph transformation rules and mass spectrometry.

Klaus Weinbauer : Interested in cheminformatics, synthesis planning, and graph-based machine learning.

Francesco Flaviano De Santis : Interested in graphs and higher-order relations. Leveraging ideas from fundamental mathematics to advance machine learning.

Benoît Goupil : Winter 2024-2025

Dr. Fabrizio Frasca : Summer 2024

Masahiro Negishi : Summer 2024

Dr. Caterina Graziani : Winter 2023-2024