Persönlicher Status und Werkzeuge

Prof. Dr. Stephan Günnemann

Assistant Professor

Data Mining and Analytics



Contact Details

Business card at TUMonline

Academic Career and Research Areas

Stephan Günnemann conducts research in the area of data mining and machine learning. The focus of his work is on the design and analysis of robust and scalable data mining techniques with the goal of being able to support the analysis and understanding of the massive amounts of data collected by science and industry. Prof. Günnemann is particularly interested in studying the principles for analyzing complex data such as networks, graphs and temporal data, with applications including clustering and anomaly detection.
He acquired his doctoral degree in 2012 at RWTH Aachen University in the field of computer science. From 2012 to 2015 he was an associate of Carnegie Mellon University, USA; initially as a postdoctoral fellow and later as a senior researcher. Prof. Günnemann has been a visiting researcher at Simon Fraser University, Canada, and a research scientist at the Research & Technology Center of Siemens AG. In 2015, Prof. Günnemann set up an Emmy Noether research group at TUM Department of Informatics. He has been a professor of data mining & analytics at TUM since 2016.


  • Member of the Emmy Noether Program of the German Research Foundation (DFG) (2015)
  • Recipient of a German Academic Exchange Service (DAAD) postdoctoral research scholarship (2012 & 2013)
  • Dissertation Award of the German Computer Science Society (2013)
  • Borchers Badge (2013)
  • Friedrich-Wilhelm Award (2009)

Key Publications (all publications)

Günnemann S, Günnemann N, Faloutsos C: „Detecting Anomalies in Dynamic Rating Data: A Robust Probabilistic Model for Rating Evolution“. ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2014: 841-850.


Günnemann S, Färber I, Boden V, Seidl T: „GAMer: A Synthesis of Subspace Clustering and Dense Subgraph Mining“. Knowledge and Information Systems. 2014; 40(2): 243-278.


Günnemann S, Färber I, Rüdiger M, Seidl T: „SMVC: Semi-Supervised Multi-View Clustering in Subspace Projections“. ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2014: 253-262.


Günnemann S, Boden B, Seidl T: „Finding Density-Based Subspace Clusters in Graphs with Feature Vectors“. Data Mining and Knowledge Discovery Journal. 2012; 25(2): 243-269.


Müller E, Günnemann S, Assent I, Seidl T: „Evaluating Clustering in Subspace Projections of High Dimensional Data“. PVLDB. 2009; 2(1): 1270-1281.