To tackle the major challenges emerging from digital technologies, the German Government has established national competence centers with focus on machine learning, big data and IT security. The map displays the leading institutions of this kind.
Competence Center Machine Learning Rhein Ruhr (ML2R)ml2r.de
ML2R aims at bringing Machine Learning technologies in Germany to a worldwide leading level. The ML2R Center is funded by the Federal Ministry of Education and Research (BMBF) as one of four nationwide ML nodes. The Technical University of Dortmund, the Fraunhofer Institutes for Intelligent Analysis and Information Systems IAIS in Sankt Augustin and for Material Flow and Logistics IML in Dortmund and the University of Bonn are involved.
Contact: Prof. Dr. Katharina Morik, Technical University Dortmund
Tel.: +49 231 755 5101
Center for Research in Security and Privacy (CRISP)crisp-da.de
The center’s research activities focus on the study of security for large systems, starting with their individual components up to their interaction within comprehensive security solutions. The research areas “Secure Internet Infrastructure” and “Secure Web Applications” are both flagship projects at CRISP. The research projects are interdisciplinary – involving various subject areas such as engineering, philosophy, physics, psychology, law and economics – and they cooperate both nationally and internationally with non-university research and industry partners.
Contact: Dr. Reiner Wichert
Tel.: +49 6151 16-27285
Helmholtz Center for Information Security (CISPA)cispa.saarland
CISPA is currently undergoing the process of becoming a new member of the Helmholtz Association of German Research Centers. It has developed from the Center for IT-Security, Privacy and Accountability, established in 2011 as a national, BMBF-funded competence center for IT security research. CISPA as a future Helmholtz center will have the critical mass of researchers (500+) to provide a comprehensive, holistic treatment of the pressing grand cybersecurity and privacy research challenges that our society faces in the age of digitalization. CISPA seeks to play a prominent international role on research, transfer, and innovation by combining cutting-edge, often disruptive foundational research with innovative application-oriented research, corresponding technology transfer, and societal outreach. It is deeply grounded in computer science and works interdisciplinarily with researchers in adjacent fields such as medicine, law, and the social sciences.
Contact: Michael Backes, Saarland University
Tel.: +49 (0)681 – 302 – 3249
Competence Center for Applied Security Technology (KASTEL)kastel.kit.edu
KASTEL is one of three competence centers for cyber security in Germany, which were initiated by the BMBF in March 2011. Following the motto “Comprehensible security in the networked world”, KASTEL is meeting the challenges posed by the increasing interconnection of previously isolated systems. Of particular importance are the consequences of digitalization in the area of critical infrastructures, for example in the energy economy, in industrial production or networked mobility, but also in „intelligent“ environments. The goal is to develop a widespread approach instead of isolated partial solutions. The focus will be on comprehensive security in specific application areas, such as power grids or intelligent factories.
Contact: Carmen Manietta, Karlsruhe Institute of Technology
Tel.: + 49 721 608-44213
TUEAI – Tubingen AI Centertuebingen.ai
The central goal of the competence center „TUEAI – Tubingen AI Center“ is to develop robust intelligent learning systems. The lack of robustness of machine learning is a central deficit of today’s algorithms. It is therefore to be expected that the development of robust learning algorithms, i.e. algorithms that still function reliably even under unfavorable conditions, will become the innovation engine for artificial intelligence in the coming years. The four pillars of the center are (1) cooperation with partners and mentors from industry, (2) a scientific focus on robust learning, (3) linking ML with other scientific disciplines, and (4) promoting societal dialogue on application and privacy aspects of ML.
Contact: Prof. Dr. Matthias Bethge, Eberhard Karls University of Tubingen
Tel.: +49 7071 29 89 017
Munich Center for Machine Learning (MCML)mcml.ai
MCML is supported by the departments of Computer Science and Statistics at LMU and TUM and aims at a connection between science and economy in the core tasks of research, qualification and knowledge transfer. MCML plans to further contribute to the successful development of applications, particularly in the areas of data science, machine intelligence and cognitive computing. The research activities have been divided into five areas of competence: Spatial and Temporal Machine Learning (ML), Learning on Graphs and Networks, Representation Learning, Model Selection, Validation and Explainable ML, and Computational Models for Large-Scale ML, which shall be applied in industry, mobility, healthcare and life sciences. By linking fundamental research with applied research, the MCML will make a significant contribution to the development in the field of ML and thus expand and strengthen the competitiveness of Germany as a location for business and science.
Contact: Prof. Dr. Thomas Seidl, Ludwig Maximilian University of München
Tel.: +49 89 2180-9191
Competence Center for Scalable Data Services and Solutions (ScaDS)scads.de
ScaDS Dresden/Leipzig was officially launched on 13 October 2014 and has laid the foundations for the creation of a national competence center for Big Data. The research project is intended to run for a period of 8 years and is driven by the partners Dresden University of Technology (TUD), the University of Leipzig (UL), the Max-Planck-Institute for Molecular Cell Biology and Genetics (MPI-CBG) and the Leibniz-Institute for Ecological and Regional Development. The competence center covers thematically important research challenges in data acquisition, data integration, knowledge extraction, visual analysis and utilization of large data sets for a broad spectrum of users.
Contact: Prof. Dr. Wolfgang E. Nagel, Technical University Dresden
Tel.: +49 (0)351 463-35450
Competence Center Machine Learning Berlin (BZML)bzml.de
By establishing a BMBF-funded competence centre, BZML aims to bundle the synergy effects of Berlin’s extraordinarily rich scientific landscape and almost 25 years of internationally groundbreaking basic research in the field of ML with the aim of achieving this goal: (1) To advance the theoretical and algorithmic foundations of ML in an internationally competitive manner, (2) to open up new scientific and technical ML applications, (3) to strive for a much simpler and better usability of ML methods for industry and the sciences as a whole, and (4) to make genuinely new research contributions in the participating sciences by jointly exploring new interdisciplinary research fields in the sciences and medicine with new challenges for ML. The BZML will also implement structures to create open platforms for knowledge and technology exchange in ML, both for industry and science.
Contact: Prof. Dr. Klaus-Robert Müller, Technical University of Berlin
Tel.: +49 30 31478620
Berlin Big Data Center (BBDC)bbdc.berlin
Our mission is to perform groundbreaking research and development, to train the „data scientists“ of tomorrow and to create solutions that facilitate the deep analysis of massive amounts of heterogeneous data sets and streams at high velocity. In order to optimally prepare industry, science and the society in Germany and Europe for the global Big Data trend, highly coordinated activities in research, teaching, and technology transfer regarding the integration of data analysis methods and scalable data processing are required. To achieve this, the Berlin Big Data Center is pursuing the following seven objectives: pooling expertise in scalable data management, conducting fundament research, developing an integrated, declarative and highly scalable open-source system, transferring technology and know-how, educating data scientists, empowering people to leverage “Smart Data” and enabling the general public to conduct sound data-driven decision-making.
Contact: Prof. Dr. Volker Markl, Technical University Berlin
Tel.: +49 30 314 23555