Stephan Günnemann

Fellowship
Rudolf Mößbauer Tenure Track

Institution
Technical University of Munich

Department
Informatics

Focus Group
Data Mining & Analytics

Short CV

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.

Selected Awards

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

Research Interest

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.

Selected Publications

  • Bojchevski, Aleksandar; Matkovic, Yves; Günnemann, Stephan: 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 )
  • Eswaran, Dhivya; Günnemann, Stephan; Faloutsos, Christos; Makhija, Disha; Kumar, Mohit: ZooBP. Proceedings of the VLDB Endowment 10 (5), 2017, 625-636 mehr… BibTeX Volltext ( DOI )
  • Boden, Brigitte; Günnemann, Stephan; Hoffmann, Holger; Seidl, Thomas: MiMAG: mining coherent subgraphs in multi-layer graphs with edge labels. Knowledge and Information Systems 50 (2), 2016, 417-446 mehr… BibTeX Volltext ( DOI )
  • Gatterbauer, Wolfgang; Günnemann, Stephan; Koutra, Danai; Faloutsos, Christos: Linearized and single-pass belief propagation. Proceedings of the VLDB Endowment 8 (5), 2015, 581-592 mehr… BibTeX Volltext ( DOI )
  • Günnemann, Stephan; Günnemann, Nikou; Faloutsos, Christos: 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 )
  • Günnemann, Stephan; Boden, Brigitte; Seidl, Thomas: DB-CSC: A Density-Based Approach for Subspace Clustering in Graphs with Feature Vectors. In: Machine Learning and Knowledge Discovery in Databases. Springer Berlin Heidelberg, 2011 mehr… BibTeX Volltext ( DOI )
  • Gunnemann, Stephan; Farber, Ines; Boden, Brigitte; Seidl, Thomas: Subspace Clustering Meets Dense Subgraph Mining: A Synthesis of Two Paradigms. 2010 IEEE International Conference on Data Mining, IEEE, 2010 mehr… BibTeX Volltext ( DOI )