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


We are excited to announce that the paper "Posterior Consistency for Missing Data in Variational Autoencoders" has been accepted at the European Conference on Machine...

Paper accepted at KDD 2023

We are excited to announce that the paper "A Higher-Order Temporal H-Index for Evolving Networks" has been accepted at the SIGKDD Conference on Knowledge Discovery and Data...

Nils Kriege im neuen Rudolphina-Artikel über die Entwicklung von KI-Verfahren für die effiziente Suche nach Wirkstoffen in großen Datenmengen.

Paper accepted at ACM FAccT

Our paper "On the Impact of Explanations on Understanding of Algorithmic Decision-Making" has been accepted at ACM FAccT.

Community Event for Students of Learning Algorithms in Wien

Paper accepted at AAMAS

The paper "Learning Constraints From Human Stop-feedback in Reinforcement Learning" has been accepted at AAMAS.