Stephan Günnemann

Prof. Stephan Günnemann is a Professor of Data Analytics and Machine Learning at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology. He leads the Munich Data Science Institute as Executive Director and directs the Konrad Zuse School of Excellence in Reliable AI. His research focuses on enhancing the reliability of machine learning systems, particularly in graph-based and temporal data analysis.

Prof. Günnemann holds a PhD from RWTH Aachen University (2012) and has held postdoctoral and senior research positions at Carnegie Mellon University (USA), Simon Fraser University (Canada), and Siemens AG. He founded the Emmy Noether Research Group at TUM in 2015 and has been recognized with prestigious awards including the Heinz Maier-Leibnitz Medal (2022) and the ACM SIGKDD Best Paper Award (2018).

His research interests span adversarial robustness, graph neural networks, and molecular data analysis. Recent work emphasizes certifiable AI safety, efficient data pruning, and uncertainty estimation in heterogeneous systems. He has contributed to over 150 peer-reviewed publications, with a focus on foundational ML challenges and real-world applications.

• Key Awards: Heinz Maier-Leibnitz Medal (2022), Google Faculty Award (2020), DFG Emmy Noether Programme (2015)
• Leadership Roles: Executive Director of Munich Data Science Institute, Director of Konrad Zuse School
• Past Roles: Postdoctoral Fellow at CMU, Researcher at Siemens

His lab actively explores cutting-edge AI topics such as graph representation learning, quantum chemistry simulations, and trustworthy ML systems. Current projects include developing certifiable defense mechanisms against adversarial attacks and scalable molecular generation frameworks.