MCML

Munich Center for Machine Learning

The Munich Center for Machine Learning is a joint research initiative of Ludwig-Maximilians-Universität München (LMU) and Technische Universität München (TUM). It is one of six nationwide AI centers in Germany to receive permanent funding as part of the German and Bavarian government's AI strategy.

Our vision is to strengthen regional, national and international competence in AI and to make the corresponding potential accessible to users from science and industry.

Latest News

09.11.2022

Article in IHK Magazin München und Oberbayern 11/2022: 'Gemeinsam schneller'

The Munich Center for Machine Learning (MCML) of the Munich universities is considered an important Bavarian competence center for artificial intelligence. To drive innovation, it is looking for companies to collaborate with.

31.10.2022

Job Offer@LMU: Science Communication Coordinator (m/f/d)

The Munich Center for Machine Learning (MCML) in collaboration with the Munich Science Communication Lab (MSCL) is looking for a Science Communication Coordinator (m/f/d) starting as soon as possible.

28.10.2022

Industrial AI Podcast: Where do we want the core AI/ML research to happen?

Industrial AI Podcast spoke with our three directors about research priorities, the battle for the best minds and the role of tech companies.

21.10.2022

Job Offer@LMU: IT System Administrator (m/f/d)

The Chair of Database Systems and Data Mining at the Institute of Informatics at LMU and the Munich Center for Machine Learning is looking for an IT System Administrator (m/f/d) starting February 01, 2023.

Our Goals

Who we are

The MCML is a joint initiative of leading researchers of Ludwig-Maximilians-Universität München (LMU) and Technische Universität München (TUM). The goal of the center is to advance foundational research in Artificial Intelligence (AI) with a strong connection to real-world applications. MCML was founded in August 2018 as a BMBF-funded competence center for machine learning. It now consists of more than 50 excellent research groups both in the field of application-oriented ML and in foundational research.

The research foci of MCML are divided into three areas: While the findings from the research focus “Foundations of Machine Learning” form the basis for methodological advances in ML, the research fields in the area of “Perception, Vision, and Natural Language Processing” represent key-enabling technologies for a variety of real-world applications, e.g., autonomous driving. The research focus “Domain-Specific Machine Learning” combines expertise from the fields of medicine, biology, physics, geosciences, and social and human sciences. In close exchange with the two other areas, ML methods are developed for application-related and socially relevant problems.

Other important components of MCML are its service, transfer, and training offers. For this purpose, MCML cooperates with other scientific institutions and companies. In addition to the training of students, there is a continuing education concept for internal and external interested parties from industry and science. This includes consulting in the area of statistics and machine learning, entrepreneurship training, and the bilateral transfer of methods and problems from industry, and training for AI users from industry.

Our Mission

In recent years, Machine Learning and Artificial Intelligence 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.

Our mission is to unite leading researchers in Germany, to strengthen regional, national and international competence in the field of machine learning and to make corresponding potential accessible to users from science and industry.

MCML Directors

Prof. Dr. 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.

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.

Research Areas

Foundations of Machine Learning

aims at strengthening the competence in (A1) Statistical Foundations and Explainability, (A2) Mathematical Foundations, and (A3) Computational Methods. These fields form the basis for all methodological advances and Munich has a high concentration of strong experts in all mentioned areas. The two universities combine some of the best Departments of Statistics, Mathematics, and Informatics in the country and are contributing significantly to the mathematical, statistical, and computational foundations of ML.

Perception, Vision, and NLP

constitutes one of the hottest areas of ML. Here, Munich offers unique strengths in Computer Vision (B1), NLP (B2) and Multi-Modal perception (B3). In the MCML, some of the leading experts in computer vision and multimodal perception from TUM join forces with some of the leading NLP researchers from LMU to develop novel ML methods that will have a significant impact on all areas of ML and data science.

Domain-specific Machine Learning

shows an immense potential in Munich, as both universities have several highly visible scientific domains with internationally renowned experts. The fields of Medicine, Biology, Physics and Geo Sciences, Social Sciences and Humanities were chosen as initial selections for collaboration, due to their scientific excellence and because there already exist strong connections in joint research and education activities. Future extensions in these and further fields are envisioned. The rich environment in the Munich scientific ecosystem inside and outside the universities facilitates translating ML concepts and technologies to many different domains.

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

mailto:contact@mcml.ai

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

Industry Collaborations

mailto:contact@mcml.ai

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

Open Source and Open Data

mailto:michel.lang@stat.uni-muenchen.de

Practical trainings for students

mailto:contact@mcml.ai

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

Statistical consulting

https://www.stablab.stat.uni-muenchen.de/index.html

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

Professional Training Courses

mailto:giuseppe.casalicchio@stat.uni-muenchen.de

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

Entrepreneurship-Training

mailto:contact@mcml.ai

We collaborate with the LMU Innovation & 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

mailto:contact@mcml.ai

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

Contact

Get in touch with us!

mailto:contact@mcml.ai

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? In case you are a student interested in working with us, please contact the relevant PI directly. We look forward to hear from you.

Ludwig-Maximilians-Universität München
Geschwister-Scholl-Platz 1
80539 München
Germany
Web

Technische Universität München
Arcisstraße 21
80333 München
Germany
Web