Keynote Speakers

• 

  Stephan Günnemann

  Professor at Technical University of Munich

  Speaker Biography

  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. He is the Executive Director of the Munich Data Science Institute and Director of the Konrad Zuse School of Excellence in Reliable AI.

• 

  Rebekka Burkholz

  Tenure-track Faculty Member at the Helmholtz Center CISPA

  Speaker Biography

  Rebekka Burkholz is a tenure-track faculty member at the Helmholtz Center CISPA, leading the Relational Machine Learning Group. Previously, Rebekka acquired a PhD in systemic risk at the ETH Risk Center Zürich and did a postdoctoral research at the Institut for Machine Learning also at the ETH Zürich.
  
  Rebekka's research combines robust algorithm design and complex network science with the quest for a theoretical understanding of deep neural networks. Based on theoretical and experimental insights, compressed models' and algorithms are developed that are robust to noise, adapt to a changing environment, and integrate information that can be available in form of small amounts of data as well as various forms of domain knowledge.