Great success for AI research in Munich

27 Jul 2022

The Munich Center for Machine Learning (MCML), a joint initiative of LMU and TUM, has been granted funding as a permanent establishment following a positive evaluation.

Initially granted temporary funding as a project, the Munich Center for Machine Learning (MCML) has successfully established itself and will receive permanent funding jointly from the German government and the state of Bavaria. As a result, regional research into artificial intelligence (AI) and particularly machine learning will gain considerable traction within the knowledge hub of Munich and beyond.

MCML is a joint undertaking by Ludwig-Maximilians-Universität (LMU) München and the Technical University of Munich (TUM). Its goal is to further advance basic research in the field of artificial intelligence (AI), with a strong focus on practical applications. MCML was founded in 2018 as one of six AI centers of excellence throughout Germany and has been funded since then by the German Federal Ministry of Education and Research (BMBF).

It now consists of more than 50 successfully operating research groups both in basic research and in the domain of application-oriented machine learning. For the centers now definitively established after their successful evaluation, BMBF and the respective state governments will jointly provide up to 100 million euros annually in total. MCML is set to receive 19.6 million euros every year.

Gave the starting signal for the MCML (from left to right): TUM Vice President Prof. Dr. Gerhard Kramer, MCML researchers Prof. Dr. Daniel Cremers (TUM) and Prof. Dr. Bernd Bischl (LMU), Dr. Rolf-Dieter Jungk (Bavarian Ministry of Science), LMU President Prof. Dr. Dr. h.c. Bernd Huber, MCML researcher Prof. Dr. Thomas Seidl (LMU) and Dr. Christoph March (BMBF).


“The AI centers of excellence are a mainstay of AI research in Germany. By consolidating their funding, we are giving the researchers planning certainty and the ability to embark on longer-term and more complex investigations when opportune. We also expect the decision to deliver fresh stimuli to the centers – especially as regards knowledge transfer, the founding of AI start-ups, and international connectivity. After all, we will only retain our technological sovereignty in AI if we bring our research results to application more quickly and cultivate sovereignty at the European level. I am convinced that the Munich AI center of excellence, with its strengths in machine learning, in spin-offs, and in key fields of application such as medicine and the humanities and social sciences, will play a major role in the achievement of this goal,” says Mario Brandenburg, Parliamentary State Secretary at the German Federal Ministry of Education and Research (BMBF).

“The digital revolution is moving into the next phase. Machine learning is playing an increasingly prominent role – including in application: industry, mobility, the care sector. In MCML, we are pooling the AI expertise of our two universities of excellence. That is wonderful teamwork from TUM and LMU,” says Markus Blume, Bavarian State Minister of Science and the Arts.

“MCML offers a very attractive scientific environment with excellent opportunities for cooperation,” says LMU President Professor Bernd Huber. “Obtaining permanent funding from the federal and state governments is a great success and testifies to the outstanding quality of MCML. It enables participating scientists in Munich to further advance their machine learning research projects.“

“The decision by federal and state governments to make MCML a permanently funded center of excellence is a clear marker of the success of our One Munich strategy. By pooling our strengths, we want to drive forward future development in the domain of artificial intelligence. This will make Munich an even stronger magnet for young talent,” says Professor Thomas F. Hofmann, President of TUM.

Three research areas at MCML

The focus of research at MCML is divided into three areas: The scientists at the center want to deepen the computational, statistical, and mathematical foundations of machine learning and research the explainability of AI – that is to say, among other things, how algorithms learn automatically with the help of vast amounts of training data and arrive at decisions.

A second focus area is “Perception, Vision, and Natural Language Processing” – in other words, how computers can extract and process information from images and natural language – key technologies for a variety of practical applications.

And finally, the third focus area is the development of machine learning methods for various socially relevant application fields – in the domains of medicine, biology, physics, geosciences, and the social sciences and humanities. In addition, MCML offers transfer and training services as well as other kinds of services. To this end, it collaborates with other scientific institutions and companies. On top of this, it educates and trains students.

“With our research activities at MCML, we’re creating new methodological foundations for the advancement and application of data science, data mining, machine learning, and artificial intelligence,” says Professor Thomas Seidl, Chair of Database Systems and Data Mining at LMU and spokesperson of MCML.

“By virtue of the close connection between basic and applied research, MCML helps new machine learning approaches reach the general public much more quickly,” says Professor Daniel Cremers, also spokesperson of MCML and Chair of Computer Vision and Artificial Intelligence at TUM.

“Through the cooperation of top-class researchers at MCML, we want to make Munich an even more attractive location for young talent from the field of machine learning,” says Professor Laura Leal-Taixé, MCML spokesperson and Professor for Dynamic Vision and Learning at TUM.

“Attracting and retaining the best and brightest minds is one of our top priorities at MCML. By offering attractive, independent research positions for young scientists, we are preparing them for a hopefully long-lasting and very successful career in academia“, says MCML spokesperson Professor Bernd Bischl, Chair of Statistical Learning and Data Science at LMU.

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