The collaborative research center SFB876 brings together data mining and embedded systems. On the one hand, embedded systems can be further improved using machine learning. On the other hand, data mining algorithms can be realized in hardware, e.g. FPGAs, or run on GPGPUs. The restrictions of ubiquitous systems in computing power, memory, and energy demand new algorithms for known learning tasks. These resource bounded learning algorithms may also be applied on extremely large data bases on servers.
Anja Karliczek, Federal Minister of Education and Research, visited the Competence Center Machine Learning Rhine-Ruhr (ML2R) together with journalists on 9 July. The Minister took the opportunity to experience practical applications of artificial intelligence and machine learning live and to try them out for herself: She met robots that make AI and ML comprehensible in a playful way, discovered AI systems that analyse spoken language, improve satellite images and make autonomous driving safer, and a swarm of drones buzzed over her. This gave the Minister impressions of outstanding projects funded by the Federal Ministry of Education and Research (BMBF) as part of the ML2R.
The CRC 876 was represented at this event with a small accompanying exhibition, which the Minister visited together with Katharina Morik.
Amal Saadallah among the finalists of the European data science and AI awards
Amal Saadallah has been selected as finalist at The European DatSci & AI Awards - Celebrating & Connecting Data Science Talent, category "Best Data Science Student of the Year". Amal works for the Research Project B3 "Data Mining in Sensor Data of Automated Processes" within the Collaborative Research Center 876.
The Data Science Award 2019 competition is open to individuals and teams working in the Data Science Ecosystem across Europe and is a unique opportunity to showcase research and application of Data Science/AI.
Sibylle Hess has successfully defended her dissertation A Mathematical Theory of Making Hard Decisions: Model Selection and Robustness of Matrix Factorization with Binary Constraints at the Chair of Artificial Intelligence. She developed new methodologies for two branches of clustering: the one concerns the derivation of nonconvex clusters, known as spectral clustering; the other addresses the identification of biclusters, a set of samples together with similarity defining features, via Boolean matrix factorization.
The members of the doctoral committee were Prof. Dr. Katharina Morik (supervisor and first examiner), Prof. Dr. Arno Siebes (second examiner, University of Utrecht) and Prof. Dr. Erich Schubert (representative of the faculty). Sibylle Hess was a research assistant at LS8, a member of the Collaborative Research Center 876 (Project C1) and now works as a postdoctoral fellow at the TU Eindhoven.
In its March edition, the Nature Career Guide recommends international researchers to move to Germany. The Collaborative Research Centres of the German Research Foundation (DFG) are named as one of ten reasons for this.
Collaborative research
Germany has more than 270 collaborative research centres that are funded by the German Research Foundation (DFG) for periods of up to 12 years, giving researchers the time to work on complex, long-term, multidisciplinary projects across universities and institutes. In 2017, the DFG spent nearly €3.2 billion on research funding. Such spending efforts are paying off, says cancer researcher Ivan Dikic, who is originally from Croatia but has been in Germany for 15 years and now heads the biochemistry department at Goethe University Frankfurt. “The German government has invested a lot more money in top-class science, and that attracts a lot of highly talented people,” he says.
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