Two new Humboldt Professorships in the field of Artificial Intelligence

5/14/2024

Prof Stefanie Jegelka and Prof Suvrit Sra are the new Humboldt Professors at the Technical University of Munich (TUM) in Artificial Intelligence. The researchers moved together from the Massachusetts Institute of Technology (MIT) in the USA to Bavaria.

TUM President Thomas Hofmann with Stefanie Jegelka and Suvrit Sra

The Humboldt Professorships aim to attract top talent from all over the world to German universities. Each professorship is endowed with up to five million euros, making it the most highly endowed research award in Germany. The Humboldt Awards for Stefanie Jegelka and Suvrit Sra were presented by Federal Research Minister Bettina Stark-Watzinger and the President of the Alexander von Humboldt Foundation, Robert Schlögl, in Berlin.

The researchers are helping to strengthen TUM's core competencies in artificial intelligence and machine learning. In particular, the Munich Data Science Institute, established as part of the Excellence Strategy and the BMBF-funded Munich Centre for Machine Learning, will benefit enormously from these top appointments.

Expert in artificial neural networks

Stefanie Jegelka, Professor of Foundations of Deep Neural Networks, researches artificial neural networks that process graphs. The idea is that the structures of social networks, financial markets, or even chemical molecules in computer science are captured by graphs. These pose a challenge for machine learning, as not only the individual points of the network but also the different connections between the points are essential. Stefanie Jegelka is working on optimizing graph neural networks to deliver reliable results.

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Humboldt Professor Stefanie Jegelka

Expert in optimization problems in machine learning

Suvrit Sra, Professor of Resource Aware Machine Learning, specializes in robust, reliable, and resource-efficient machine learning methods. His research focuses, in particular, on solving optimization problems for machine learning with multiple parameters. For example, these complex optimization problems are used in autonomous driving so that a car can reliably distinguish a sign from a person.

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The crème de la crème of science

At the award ceremony, TUM President Prof. Thomas F. Hofmann said: ‘TUM is thus strategically strengthening its internationally leading core competencies in machine learning and artificial intelligence. It is not buildings and equipment but unique people who bring the new into the world with their talents, courageous visions, pioneering spirit, and bold research approaches. And the Alexander von Humboldt Professorships are an effective pheromone for attracting this crème de la crème of science to Germany.’

Further information and links

Further Humboldt Professorships at TUM:

2009: Bioinformatics specialist Prof. Burkhard Rost (Columbia University New York/USA)
2010: Communications Engineering expert Prof. Gerhard Kramer (University of Southern California/USA)
2011: Business IT specialist Prof. Hans-Arno Jacobsen (University of Toronto/Canada)
2012: Diabetes expert Prof. Matthias Tschöp (University of Cincinnati/USA)
2013: Mathematician and Operations Research expert Prof. Andreas S. Schulz (MIT, Boston/USA)
2018: Information scientist Prof. Marco Caccamo (University of Illinois at Urbana-Champaign/USA)
2020: Prof. Daniel Rückert, expert on the implementation of AI in medicine (Imperial College London/United Kingdom)
2021: Prof. Angela Schöllig, expert in Robotics and Artificial Intelligence (University of Toronto/Canada)

The Munich Data Science Institute (MDSI) is an integrative research institute of TUM. These cross-sectional institutes are each dedicated to a technologically and socially highly relevant field of science. The MDSI conducts research on mathematical and computer science questions of data analysis and develops new theories and methods of machine learning and artificial intelligence. From this, it develops applications for the various research fields at TUM, such as personalized medicine, life sciences, aerospace, mobility, additive manufacturing, materials science, quantum research and climate research.

The Munich Center for Machine Learning (MCML) is a joint research initiative of TUM and LMU and one of six national AI competence centers in Germany. The center consists of over 45 research groups and has the goal to strengthen foundational research in machine learning with a strong focus on real-world applications. The MCML is permanently funded by the German federal government and the state of Bavaria. It is an integral part of the German AI strategy and Hightech Agenda Bavaria.

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