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.
Anice Jahanjoo : Machine learning in Health Monitoring Systems and SAR Image processing.
Pascal Welke : Interested in similarity based learning on graphs, as well as counting of various substructures for fun and (machine learning) profit.
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.