Stephan Günnemann 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.
2017 Junior-Fellow of the German Computer Science Society
2017 Microsoft Azure Research Award
2015 Member of the Emmy Noether Program of the German Research Foundation (DFG)
2013 Recipient of a German Academic Exchange Service (DAAD) Research Fellowship
2013 Dissertation Award of the German Computer Science Society
2013 Borchers Badge for Doctoral Dissertation, RWTH Aachen University
2012 Recipient of a German Academic Exchange Service (DAAD) Research Fellowship
2011 Best Paper Award at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
2009 Friedrich-Wilhelm Award, RWTH Aachen University
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.
- Robust Spectral Clustering for Noisy Data. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '17, ACM Press, 2017 mehr… BibTeX Volltext ( DOI )
- MiMAG: mining coherent subgraphs in multi-layer graphs with edge labels. Knowledge and Information Systems 50 (2), 2016, 417-446 mehr… BibTeX Volltext ( DOI )
- Linearized and single-pass belief propagation. Proceedings of the VLDB Endowment 8 (5), 2015, 581-592 mehr… BibTeX Volltext ( DOI )
- Detecting anomalies in dynamic rating data. Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '14, ACM Press, 2014 mehr… BibTeX Volltext ( DOI )