The European Lab for Learning and Intelligent Systems (ELLIS) has added ELLIS Munich to its network of independent units performing outstanding basic research in artificial intelligence (AI) and machine learning (ML). The newly-createdELLIS Munich unit, officially presented in a virtual launch event on September 15th together with 30 other units, integrates expertise from Helmholtz Zentrum München (HMGU), the Technical University of Munich (TUM) and draws on the strengths of the Munich Data Science Institute and the Helmholtz AI network.
ELLIS Munich aims to develop novel machine learning methods and deploy them in the fields of biomedicine, computer vision and earth observation. “ELLIS Munich scientists will tackle the great challenges of AI explicability and the development of reliable, intelligent algorithms” says Daniel Cremers (TUM), spokesperson and co-director of the unit together with Fabian Theis (Helmholtz AI - HMGU) and Massimo Fornasier (TUM). The unit will contribute to the ELLIS programs on Computer Vision, Health and Earth & Climate.
Recruiting talent
Beyond the directors, the foundational core faculty of ELLIS Munich includes five ELLIS members: Stephan Günnemann, Laura Leal-Taixé, Julia Schnabel,Volker Tresp and Xiaoxiang Zhu. The faculty team also counts on renowned researchers Bernd Bischl, Heinrich Schütze, Mathias Drton, Patrick van der Smagt, Matthias Nießner, Daniel Rückert and Eleftheria Zeggini, and is expected to grow over time.
The extended faculty will comprise associated ELLIS members active in applied ML to build a thriving community. ELLIS Munich will also spearhead a recruitment drive for attracting top talent, as TUM and HMGU are major beneficiaries of Bavaria’s € 2bn High-Tech Agenda, a unique AI investment scheme in Germany linking AI and SuperTech to remake Bavaria as an “AI District”.
Building expertise and collaborations on four key areas
ELLIS Munich’s activities will cover four areas of expertise: foundations of machine learning (deep learning on graphs and time series, optimal control, knowledge graphs, novel training methods), biomedicine (with applications in human functional genomics, biomedical imaging and bioengineering, drug research and electronic health records), earth observation (focusing on elaboration of remote sensing data) and computer vision (with applications such as image restoration, multiple-view reconstruction, visual SLAM, dynamic scene understanding and multiple object tracking).
ELLIS, the European network of excellence in AI and machine learning
ELLIS is a pan-European effort initiated in 2018. Its central goal is to foster European research excellence in machine learning and related fields by offering scientists outstanding opportunities to carry out their research in Europe, and to train the next generation of young European researchers in this field of strategic importance. It focuses particularly on tackling fundamental research challenges in the field of AI that promote positive economic and societal impacts. At present, research topics within the ELLIS network include fundamental machine learning, computer vision, natural language processing, robotics, human-centric and trustworthy AI, and areas of application such as environmental modeling, autonomous systems design, biology, and health.
The ELLIS units were selected on the basis of scientific excellence by a committee of leading scientists from several different countries. They are envisioned as new working environments where outstanding researchers are enabled to combine cutting-edge research with the creation of start-ups and industrial impact. In total, they have committed funding of some 300 million euros for an initial period of five years. Twenty percent of this sum will go toward ELLIS network activities such as student and faculty exchanges and the organization of joint ELLIS research programs and workshops.
So far, the network includes 28 units from 14 different countries that work closely together in research, training and infrastructure.
More information
Watch ELLIS Munich unit presentation video