Uniting leading researchers in Germany, to strengthen regional, national and international competence in the field of machine learning and making corresponding potential accessible to users from science and industry.

The Munich Center for Machine Learning

In recent years, Machine Learning (ML) and Artificial Intelligence (AI) have become essential key technologies in all areas of our lives. The world would benefit massively from these new technologies. However, the migration of technologies from science to practice still remains a tough challenge.

The Munich Center for Machine Learning (MCML) is made up of leading researchers from the Ludwig-Maximilians-Universität in Munich (LMU Munich) and the Technical University in Munich (TU Munich). They are experts in the fields of data science, computer science and statistics. The MCML is funded by the Federal Ministry of Education and Research (BMBF) as one of six nationwide centers for AI and ML research and transfer.

Pursuing the goal of strenghtening regional, national and international competence in the field of machine learning, MCML’s fundamental research is bundled in six profile areas:


MCML Directors


Prof. Dr. Thomas Seidl

is a professor for Computer Science and head of the Database Systems and Data Mining Group at LMU Munich. His fundamental research on data mining and database technologies with application domains in engineering, business, life science and humanities yielded more than 300 scientific publications so far. He serves on many program committees and scientific boards and is co-chair of the LMU Data Science Lab, the ZD.B Innovation Lab, the Munich School of Data Science @ Helmholtz, TUM & LMU (MuDS) and of the elite Master program in Data Science at LMU.


Prof. Dr. Bernd Bischl

Bernd Bischl holds the chair of "Statistical Learning and Data Science" at the Department of Statistics at the Ludwig-Maximilians-Universität in Munich. He studied Computer Science, Artificial Intelligence and Data Sciences in Hamburg, Edinburgh and Dortmund and obtained his Ph.D from Dortmund Technical University in 2013 with a thesis on "Model and Algorithm Selection in Statistical Learning and Optimization". His research interests include AutoML, model selection, interpretable ML, as well as the development of statistical software. He is a member of ELLIS in general, and a faculty member of ELLIS Munich, an active developer of several R-packages, leads the "mlr" (Machine Learning in R) engineering group and is co-founder of the science platform "OpenML" for open and reproducible ML. Furthermore, he leads the Munich branch of the Fraunhofer ADA Lovelace Center for Analytics, Data & Applications, i.e. a new type of research infrastructure to support businesses in Bavaria, especially in the SME sector.


Prof. Dr. Daniel Cremers

holds the chair for Computer Vision and Artificial Intelligence at TU Munich since 2009. In 2002 he obtained a PhD in Computer Science from the University of Mannheim, Germany. Subsequently he spent two years as a postdoctoral researcher at the University of California, Los Angeles (UCLA) and one year as a permanent researcher at Siemens Corporate Research in Princeton, NJ. From 2005 until 2009 he was associate professor at the University of Bonn, Germany. In 2016, Prof. Cremers received the Gottfried Wilhelm Leibniz Award, the biggest award in German academia.

Profile Areas

Spatial and Temporal Machine Learning

Prof. Dr. Nassir Navab, Prof. Dr. Volker Schmid, Prof. Dr. Matthias Schubert

Spatial and spatio-temporal data, i.e. data with a dynamic spatial or time component such as sensor data or camera recordings, play an important role [...]

Learning on Graphs and Networks

Prof. Dr. Stephan Günnemann, Prof. Dr. Göran Kauermann, Prof. Dr. Volker Tresp

In many data-intensive applications, from social media, genome research to mobility, attributed graphs and networks have proven to [...]

Representation Learning

Prof. Dr. Moritz Große-Wentrup, Prof. Dr. Hinrich Schütze, Prof. Dr. Dr. Fabian Theis

The research field of representation learning involves the automated generation of meaningful features from high-dimensional data sets [...]

Automatic and Explainable Modeling

Prof. Dr. Bernd Bischl, Prof. Dr. Anne-Laure Boulesteix, PD Dr. Fabian Scheipl

Valid benchmarking of machine learning methods is essential to gain robust guarantees for the practical use of models [...]

Computational Models for Large-Scale Machine Learning

Prof. Dr. Christian Böhm, Prof. Dr. Peer Kröger, Prof. Dr. Thomas Seidl

The profile area “computational models for large-scale machine learning” focuses on several topics in the field of unsupervised machine learning [...]

Computer Vision

Prof. Dr. Daniel Cremers, Prof. Dr. Laura Leal-Taixé, Prof. Dr. Matthias Nießner

This profile area is focused on a number of projects in the area of computer vision that revolve around the challenge of going beyond the classical neural networks and generalizing them in various ways [...]


The MCML is working on cutting-edge research projects in different areas of machine learning

Services and Staff

The MCML integrates several services, intended for collaboration and outreach to other research institutions, industry and startups.

General Manager

Dr. Elke Achtert


The general manager coordinates all activities within MCML and maintains relationships with partners outside the center.

Industry Collaborations


The MCML is actively looking for collaborations at the intersections of research and industry. For additional information, contact us!

Open Source and Open Data


We want to actively contribute to the world of open source and open data. This means that we aim to share results of our research in an open source form.

Practical trainings for students


The MCML offers in-depth practical trainings for students based on real industry or research questions and use cases.

Statistical consulting


The statistical consulting unit at LMU will integrate a wide variety of machine learning techniques into its portfolio.

Professional Training Courses


Machine Learning and programing skills are widely required in the digital era. We want to enable industry and research partners with those skills.



We collaborate with the LMU Entrepreneurship Center to actively give our members and partners the opportunity to create a business out of research findings or their own ideas.

Research Coordinator TUM


We aim to bring recent research advances from the field of machine learning into other domains by teaching university students.


Get in touch with us!


Are you interested in working with us, either as a researcher or in form of an industry or research cooperation? Do you see valuable use-cases for Machine Learning in your company? Are you a journalist reporting on the future of Machine Learning and Artificial Intelligence? We look forward to hear from you via e-mail.

Munich Center for Machine Learning

Ludwig-Maximilians-Universität in Munich
Institute for Informatics
Oettingenstr. 67
80538 München
Ludwig-Maximilians-Universität in Munich
Institute for Statistics
Ludwigstr. 33
80539 München
Technical University of Munich
Department of Informatics
Boltzmannstr. 3
85748 Garching