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MaLGa Colloquia - The World of Graph Neural Networks: From the Mystery of Generalization to Foundational Limitations




MaLGa Colloquia - The World of Graph Neural Networks: From the Mystery of Generalization to Foundational Limitations


Gitta Kutyniok - Ludwig-Maximilians Universität München


The tremendous importance of graph structured data due to recommender systems or social networks led to the introduction of graph neural networks (GNNs). After a general introduction to GNNs, we will discuss results about their amazing generalization capabilities. We will study the more specialized question to which extent GNNs are able to generalize to graphs, which describe a similar phenomenon as present in the training data set, as well as the fully general problem. We will finish with a word of caution when training GNNs on classical digital hardware, and present fundamental limitations.


Gitta Kutyniok currently has a Bavarian AI Chair for Mathematical Foundations of Artificial Intelligence at the Ludwig-Maximilians Universität München. She received her Diploma in Mathematics and Computer Science as well as her Ph.D. degree from the Universität Paderborn in Germany, and her Habilitation in Mathematics in 2006 at the Justus-Liebig Universität Gießen. From 2001 to 2008 she held visiting positions at several US institutions, including Princeton University, Stanford University, Yale University, Georgia Institute of Technology, and Washington University in St. Louis. In 2008, she became a full professor of mathematics at the Universität Osnabrück, and moved to Berlin three years later, where she held an Einstein Chair in the Institute of Mathematics at the Technische Universität Berlin and a courtesy appointment in the Department of Computer Science and Engineering until 2020. In addition, Gitta Kutyniok holds an Adjunct Professorship in Machine Learning at the University of Tromso since 2019.

Gitta Kutyniok has received various awards for her research such as an award from the Universität Paderborn in 2003, the Research Prize of the Justus-Liebig Universität Gießen and a Heisenberg-Fellowship in 2006, and the von Kaven Prize by the DFG in 2007. She was invited as the Noether Lecturer at the ÖMG-DMV Congress in 2013, a plenary lecturer at the 8th European Congress of Mathematics (8ECM) in 2021, and the lecturer of the London Mathematical Society (LMS) Invited Lecture Series in 2022. She was also honored by invited lectures at both the International Congress of Mathematicians 2022 (ICM 2022) and the International Congress on Industrial and Applied Mathematics (ICIAM 2023). Moreover, she was elected as a member of the Berlin-Brandenburg Academy of Sciences and Humanities in 2017 and of the European Academy of Sciences in 2022, and became a SIAM Fellow in 2019. She is currently the main coordinator of the Research Focus "Next Generation AI" at the Center for Advanced Studies at LMU and the DFG-Priority Program "Theoretical Foundations of Deep Learning", serves as Vice President-at-Large of SIAM, and acts as Co-Director of the Konrad Zuse School of Excellence in Reliable AI (relAI) in Munich.

Gitta Kutyniok's research work covers, in particular, the areas of applied and computational harmonic analysis, artificial intelligence, compressed sensing, deep learning, imaging sciences, inverse problems, and applications to life sciences, robotics, and telecommunication.


June 5th 2023, 16:00


Room 704, UniGe DIMA, Via Dodecaneso 35

Streaming available at the link below.