The research group Data Mining and Machine Learning investigates novel approaches to exploratory data analysis, unsupervised, semi-supervised and supervised learning. We focus on methods for various data types including texts, graphs, high-dimensional feature vectors and other complex structures. We consider different tasks, e.g., representation learning, embedding, clustering, causality detection, classification and reinforcement learning.

The research group Data Mining and Machine Learning consists of four work groups:

Our methods are inspired by challenges arising from different application areas, e.g. medicine, neuroscience, pharmacoinformatics, renewable energies and social sciences.

Team of research group DM

Publications of research group DM

Projects of research group DM


Paper accepted at KDD 2024

Our paper "Attacking Graph Neural Networks with Bit Flips: Weisfeiler and Leman Go Indifferent" has been accepted at the SIGKDD Conference on Knowledge Discovery and Data...

Warum Künstliche Intelligenz gar nicht so intelligent ist, die meisten Daten ungenutzt herum liegen und wie man bei all diesen rasanten Entwicklungen am Ball bleibt, darüber...

Auf KI basierende Bild- und Textgeneratoren produzieren digitale Werke von verblüffender Qualität, die manche Menschen beunruhigen: Wird Kunst bald automatisiert? Rudolphina...

Paper accepted at JMLR

Our paper "Granger Causal Inference in Multivariate Hawkes Processes by Minimum Message Length" has been accepted at the Journal of Machine Learning Research.


We are excited to announce that the 22nd International Workshop on Mining and Learning with Graphs (MLG) will again be held jointly with the ECML PKDD this September (either...