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Technische Universität München

Prof. Dr. Stephan Günnemann

Technical University of Munich
Department of Computer Science - I26
Boltzmannstr. 3
85748 Garching b. München
Germany

Room: 00.11.059
Phone: +49 (0)89 / 289-17282
E-Mail: s.guennemann@tum.de

Research Focus

  • Machine Learning for Graphs/Networks, Graph Neural Networks
  • Reliable Machine Learning, Robust and Adversarial Machine Learning, Uncertainty Estimation
  • ML for Science: e.g. molecules, simulations, etc.
  • Machine Learning for Sequential Data (text/LLMs, time series)

Current Positions

  • Executive Director of the Munich Data Science Institute
    www.mdsi.tum.de
  • Director of the Konrad Zuse School of Excellence in Reliable AI
    https://zuseschoolrelai.de/
  • Professor (W3, tenured) for Data Analytics and Machine Learning

Professional Experience

  • Director of the Konrad Zuse School of Excellence in Reliable AI
    since June 2022
  • Executive Director of the Munich Data Science Institute
    since October 2020
  • Professor
    Technische Universität München, Munich, Germany 
    since October 2016
  • Research Group Leader
    Technische Universität München, Munich, Germany 
    July 2015 - September 2016
    • funded by the Emmy Noether Program of the German Research Foundation (DFG)
  • Research Scientist
    Siemens AG, Siemens Research & Technology Center, Munich, Germany 
    February 2015 - June 2015
  • Senior Researcher
    Carnegie Mellon University, Pittsburgh, USA 
    October 2014 - February 2015
  • Post-Doctoral Researcher
    Carnegie Mellon University, Pittsburgh, USA 
    October 2012 - September 2014
  • Visiting Researcher
    Simon Fraser University, Vancouver, Canada 
    May 2011 - June 2011
  • Research Associate 
    Data Management and Data Exploration Group, RWTH Aachen University 
    July 2008 - September 2012
  • Promotion [Ph.D.] at the RWTH Aachen University (June 2008 - March 2012)
    • Dissertation (Ph.D. thesis) "Subspace Clustering for Complex Data"
    • Graduated with distinction "summa cum laude"
  • Studies in Computer Science at RWTH Aachen Universiy (October 2003 - May 2008)
    • Diplomarbeit (Master thesis) "Approximations for efficient subspace clustering in high-dimensional databases"
    • Graduated with distinction

Academic Honors and Awards

  • Best Paper Award for the paper "Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry" at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2023
  • Heinz Maier-Leibnitz Medal, the highest scientific award of TUM, 2022
  • Google Faculty Research Award in Machine Learning, 2019/2020
  • Best Research Paper Award at the ACM SIGKDD Conference on Knowledge Discovery and Data Mining for the paper ``Adversarial Attacks on Neural Networks for Graph Data'', 2018
  • Microsoft Azure Research Award, 2017
  • Rudolf Mößbauer Fellowship of the TUM Institute for Advanced Study, 2016
  • Emmy Noether Research Grant of the German Research Foundation (DFG) to set up an independent research group, 2015
  • Young Researcher at the Heidelberg Laureate Forum, Heidelberg, Germany, 2015
  • Best Student Paper Runner-Up Award for the paper ``Com2: Fast Automatic Discovery of Temporal ('Comet') Communities`` at the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2014
  • Dissertation Award of the German Computer Science Society, Section on Databases and Information Systems, 2013
  • DAAD Scholarship for postdoctoral research at the Carnegie Mellon University for the period 10/2013 to 09/2014
  • Borchers-Plakette for doctoral dissertation "Subspace Clustering for Complex Data", 2013
  • Travel Award for the paper ``Finding Contexts of Social Influence in Online Social Networks`` at the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Workshop on Social Network Mining and Analysis, 2013
  • DAAD Scholarship for postdoctoral research at the Carnegie Mellon University for the period 10/2012 to 09/2013
  • Best Paper Award for the paper "DB-CSC: A density-based approach for subspace clustering in graphs with feature vectors" at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2011
  • Friedrich-Wilhelm-Preis for diploma thesis on "Approximations for efficient subspace clustering in high-dimensional databases", 2009
  • Springorum-Denkmünze for Diplom (Master of Science) in Computer Science with overall mark 'excellent', 2009
  • Schöneborn-Preis for the year's best Vordiplom (Bachelor degree equivalent) in Computer Science at the RWTH Aachen University, 2006
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