Prof. Dr. Stephan Günnemann

Department

Informatics

Academic Career and Research Areas

Stephan Günnemann conducts research in the area of machine learning and data analytics. His main research focuses on how to make machine learning techniques reliable, thus, enabling their safe and robust use in various application domains. Prof. Günnemann is particularly interested in studying machine learning methods targeting complex data domains such as graphs/networks 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 at TUM since 2016.

Awards

  • Google Faculty Research Award in Machine Learning (2020)
  • 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)

Key Publications (alle Publikationen)

Bojchevski A, Klicpera J, Günnemann S: "Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More". International Conference on Machine Learning. 2020.

Abstract

Klicpera J, Weißenberger S, Günnemann S: "Diffusion improves graph learning". Advances in Neural Information Processing Systems. 2019; 13354-13366.

Abstract

Bojchevski A, Günnemann S: "Certifiable Robustness to Graph Perturbations". Advances in Neural Information Processing Systems. 2019; 8319-8330.

Abstract

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