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 seven work groups:
- Univ.-Prof. Dr. Claudia Plant: Data Mining
- Assoz. Prof. Dr. Christian Böhm : Database Techniques for Data Mining
- Assoz. Prof. Dr. Nils Kriege: Machine Learning with Graphs
- Univ.-Prof. Dr. Benjamin Roth: Natural Language Processing
- Assoz. Prof. Dr. Sebastian Tschiatschek: Probabilistic and Interactive Machine Learning
- Ass.-Prof. Dr. Sebastian Schuster: Natural Language Processing, AI and Computational Semantics
- Ass.-Prof. Dr. Yllka Velaj: Scalable Algorithms for Graph Mining
Our methods are inspired by challenges arising from different application areas, e.g. medicine, neuroscience, pharmacoinformatics, renewable energies and social sciences.
