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 machine learning techniques with the goal to enable a reliable analysis 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.
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.
- ACM SIGKDD Best Research Paper Award (2018)
- Junior-Fellow of the German Computer Science Society (2017)
- 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)
Key Publications (alle Publikationen)
Zügner D, Akbarnejad A, Günnemann S: "Adversarial Attacks on Neural Networks for Graph Data". ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2018: 2847-2856.Abstract
Bojchevski A, Shchur O, Zügner D, Günnemann S: "NetGAN: Generating Graphs via Random Walks". International Conference on Machine Learning. 2018; 609-618.Abstract
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.Abstract
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.Abstract
Müller E, Günnemann S, Assent I, Seidl T: „Evaluating Clustering in Subspace Projections of High Dimensional Data“. PVLDB. 2009; 2(1): 1270-1281.Abstract