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Mission Statement and Goals

BIFOLD, the Berlin Institute for the Foundations of Learning and Data aims to conduct research into the scientific foundations of Big Data and Machine Learning, to advance AI application development, and greatly increase the impact to society, the economy, and science.

BIFOLD will pursue the following strategic priorities in line with the German National AI Strategy:

• Research: Conduct high-impact foundational research in the fields of Big Data, Machine Learning and their intersection, to profoundly advance the state-of-the-art in Big Data and Machine Learning methods and technologies as well as attract the world’s best scientists to Germany.
• Innovation: Prototype AI technologies, Big Data systems, Data Science tools, Machine Learning algorithms, and support knowledge and technology exchange, to empower innovation in the sciences, humanities, and companies, particularly, startups.
• Education: Prepare the next generation of experts in Big Data and Machine Learning, for future academic or industrial careers.

Directors

INSTITUTIONAL PARTNERS

Latest Publications

• Ilja Behnke, Lukas Pirl, Lauritz Thamsen, Robert Danicki, Andreas Polze, and Odej Kao:
  “Interrupting Real-Time IoT Tasks: How Bad Can It Be to Connect Your Critical Embedded System to the Internet?”
  To appear in the Proceedings of the 39th IEEE International Performance Computing and Communications Conference (IPCCC). IEEE, 2020
  
• J. Traub, P. M. Grulich, A. R. Cuéllar, S. Breß, A. Katsifodimos, T. Rabl, and V. Markl:
  “Scotty: General and Efficient Open-Source Window Aggregation for Stream Processing Systems”
  ACM Transactions on Database Systems (TODS), 2020
  
• Lennart Tautz, Hannu Zhang, Markus Hüllebrand, Matthias Ivantsits, Sebastian Kelle, Titus Kuehne, Volkmar Falk und Anja Hennemuth:
  “Cardiac radiomics: an interactive approach for 4D data exploration”
  Directions in Biomedical Engineering, Band 6, Heft 1, 2020
  https://doi.org/10.1515/cdbme-2020-0008
  
• Watkins, T.B.K., Lim, E.L., Petkovic, M., Schwarz, R. et al.:
  “Pervasive chromosomal instability and karyotype order in tumour evolution”
  Nature 587, 126–132, 2020
  https://doi.org/10.1038/s41586-020-2698-6
  
• Anja Feldmann, Oliver Gasser, Franziska Lichtblau, Enric Pujol, Ingmar Poese, Christoph Dietzel, Daniel Wagner, Matthias Wichtlhuber, Juan Tapiador, Narseo Vallina-Rodriguez, Oliver Hohlfeld, and Georgios Smaragdakis:
  “A view of Internet Traffic Shifts at ISP and IXPs during the COVID-19 Pandemic”
  Internet Architecture Board COVID-19 Network Impacts Workshop, 2020.
  
• Rank, N.; Pfahringer, B.; Kempfert, J.; Stamm, C.; Kühne, T.; Schoenrath, F.; Falk, V.; Eickhoff, C.; Meyer, A.:
  “Deep-learning-based real-time prediction of acute kidney injury outperforms human predictive performance”
  npj Digit. Med. 3, 139 (2020).
  https://doi.org/10.1038/s41746-020-00346-8
  
• Mihail Bogojeski, Leslie Vogt-Maranto, Mark E. Tuckerman, Klaus-Robert Müller, Kieron Burke:
  “Quantum chemical accuracy from density functional approximations via machine learning“
  Nature Communications 11:5223, 2020
  https://doi.org/10.1038/s41467-020-19093-1
  
• Dong-Ok Won, Klaus-Robert Müller, Seong-Whan Lee:
  “An adaptive deep reinforcement learning framework enables curling robots with human-like performance in real-world conditions“
  Science Robotics Vol. 5, Issue 46, eabb9764. September 23, 2020
  DOI: 10.1126/scirobotics.abb9764
  
• Philipp Leinen, Malte Esders, Kristof T. Schütt, Christian Wagner, Klaus-Robert Müller, F. Stefan Tautz:
  “Autonomous robotic nanofabrication with reinforced learning“
  Science Advances Vol. 6, No. 36, 2020
  
• Anja Feldmann, Oliver Gasser, Franziska Lichtblau, Enric Pujol, Ingmar Poese, Christoph Dietzel, Daniel Wagner, Matthias Wichtlhuber, Juan Tapiador, Narseo Vallina-Rodriguez, Oliver Hohlfeld, and Georgios Smaragdakis:
  “The Lockdown Effect: Implications of the COVID-19 Pandemic on Internet Traffic”
  ACM IMC 2020