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09/15/2020 15:25

Virtual Summer School on Machine Learning excites Global Research Community

Inga Daase Presse und Öffentlichkeitsarbeit
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS

    Over 830 registered participants from 64 countries: The Summer School on Machine Learning with restricted resources was well received by the international research community. The program included lectures, interactive formats and opportunities for networking and getting to know one another. A hackathon on a current task from the field of logistics ran in parallel. For the first time, the Summer School took place as an online event and was jointly organized by the Competence Center Machine Learning Rhine-Ruhr (ML2R) and the Collaborative Research Center 876 "Providing Information by Resource-Constrained Data Analysis" of the Faculty of Computer Science at TU Dortmund University.

    The international Summer School "Resource-aware Machine Learning", which took place from August 31st to September 4th as an online event, focused on the topics of Machine Learning (ML) and data analysis with limited resources. "This year's summer school has increased the international attention for our cutting-edge research on resource-aware Machine Learning methods. I am particularly pleased that we have now been able to promote young scientists and inform interested parties from various professions on a global platform," sums up Prof. Dr. Katharina Morik, spokeswoman of the Competence Center ML2R and the Collaborative Research Center 876.

    Machine Learning is already the key to many Artificial Intelligence technologies. With the help of ML methods, it is possible to extract information and knowledge from large data sets. The constantly growing volume of data offers great potential, but also poses a central problem for Machine Learning: limited resources, such as computational power, communication restrictions and energy constraints.

    A versatile program offered insights into the latest ML research
    A new online format allowed the organizing cooperation partners - the Competence Center ML2R and the Collaborative Research Center 876 - to welcome a broad international audience to the Summer School. From Peru to Morocco, from Finland to India, from Russia to Australia: More than 830 registered participants from 64 countries gained fascinating insights into the latest ML research at ML2R and enriched the interactive program items with experiences from their respective nations’ scientific and industrial landscapes.

    The Summer School offered them a variety of opportunities for joint learning and networking. In pre-recorded and live lectures, scientists from ML2R, SFB 876 and external research institutions presented their latest research results in the field of Machine Learning. In Q&A sessions and a panel discussion with Prof. Dr. Katharina Morik, the participants interacted with the lecturers. In the virtual meeting place "Students' Corner", young researchers were able to present their own research and develop it further in an exchange of ideas.

    Social evening events complemented the program and allowed the researchers to get to know each other, whether it was through speed dating, chatting or playing games. Following the motto of resource efficiency, the participants for example had the opportunity to cook together virtually and exchange resource-efficient recipes.

    Controlling logistics robots remotely
    An accompanying hackathon with a logistics task rounded off the Summer School program. Logistics is an important application field of Machine Learning because a variety of optimization tasks arise that benefit from ML research. The task of the hackathon was to produce the most accurate position predictions possible for transport robots, which were located on a logistics test field at the TU Dortmund University. To solve the task, the participants were able to access sensor data from the Dortmund test field to train their ML models.

    The conclusion and highlight of the Summer School was a live broadcast from the Dortmund logistics hall. The finalists of the hackathon were allowed to control the logistics robots from a distance and steer them over the test field. ML2R congratulates Mirco Hünnefeld, doctoral student at the TU Dortmund University, on winning the hackathon. The scientist was able to generate position predictions for the robots, which on average only deviated 15 cm from the actual position.

    The Competence Center ML2R and the Collaborative Research Center 876 would like to thank all speakers, cooperation partners and its committed participants for a successful Summer School. For all those who could not attend the Summer School or who would like to experience some of the highlights again, selected lectures are available online.

    About the Competence Center Machine Learning Rhine-Ruhr (ML2R)
    The Competence Center ML2R conducts cutting-edge ML research, supports young scientists and strengthens technology transfer in companies. ML2R is one of the six German Competence Centers for Artificial Intelligence and is funded by the Federal Ministry of Education and Research (BMBF). The TU Dortmund University, the University of Bonn, the Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS in Sankt Augustin and the Fraunhofer Institute for Material Flow and Logistics IML in Dortmund are involved. Speakers of the Center are Prof. Dr. Katharina Morik (TU Dortmund University) and Prof. Dr. Stefan Wrobel (Fraunhofer IAIS/University of Bonn).

    More information: Competence Center Machine Learning Rhine-Ruhr (ML2R) Summer School on resource-aware Machine Learning Lectures of the Summer School

    Criteria of this press release:
    Journalists, Scientists and scholars, Students
    Information technology, Mathematics
    transregional, national
    Advanced scientific education, Studies and teaching

    Conclusion and highlight of the Summer School: a live broadcast from the Dortmund logistics hall. The finalists of the hackathon were allowed to control the logistics robots from a distance and steer them over the test field.

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