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

• R. Levie, Ç. Yapar, G. Kutyniok, G. Caire:
  „Pathloss Prediction using Deep Learning with Applications to Cellular Optimization and Efficient D2D Link Schedulin“
  IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020
  
• Erwin Quiring, David Klein, Daniel Arp, Martin Johns, Konrad Rieck:
  „Adversarial Preprocessing: Understanding and Preventing Image-Scaling Attacks in Machine Learning“
  Proc. of the 29th USENIX Security Symposium, to appear August 2020
  
• Niklas Semmler, Matthias Rost, Georgios Smaragdakis, Anja Feldmann:
  „Edge Replication Strategies for Wide-Area Distributed Processing“
  ACM EdgeSys 2020
  
• Lauritz Thamsen, Jossekin Beilharz, Vinh Thuy Tran, Sasho Nedelkoski, Odej Kao:
  „Mary, Hugo, and Hugo*: Learning to Schedule Distributed Data-Parallel Processing Jobs on Shared Clusters“
  Concurrency and Computation: Practice and Experience (to appear). Wiley, 2020
  
• Philipp Benner, Martin Vingron:
  „ModHMM: A modular supra-Bayesian genome segmentation method.“
  Journal of Computational Biology 27.4.: 442-457, 2020
  
• Valleriani, Matteo (ed.):
  „De sphaera of Johannes de Sacrobosco in the Early Modern Period: The Authors of the Commentaries.“
  Springer Nature, 2020
  https://doi.org/10.1007/978-3-030-30833-9
  
• D. A. Awan, Renato L.G. Cavalcante and Slawomir Stanczak:
  „Robust Cell-Load Learning with a Small Sample Set.“
  IEEE Transactions on Signal Processing (TSP), 68:270-283., 2020
  
• F. Ganji, S. Amir, S. Tajik, D. Forte, J.-P. Seifert:
  „Machine Learning Based Adversary Modeling for Composed Hardware„
  To appear in Design, Automation and Test (DATE), 2020
  
• Schütt, K.T.; Chmiela, S.; von Lilienfeld, O.A.; Tkatchenko, A.; Tsuda, K.; Müller, K.-R. (Eds.):
  „Machine Learning Meets Quantum Physics“
  Lecture Notes in Physics, Springer, 2020
  
• Lutz, Clemens; Breß, Sebastian; Zeuch, Steffen; Rabl, Tilman; Markl, Volker:
  „Pump Up the Volume: Processing Large Data on GPUs with Fast Interconnects“
  accepted at ACM SIGMOD/PODS International Conference on the Management of Data, 2020
  
• Sattler, Felix; Müller, Klaus-Robert; Wiegand, Thomas; Samek, Wojciech:
  „On the Byzantine Robustness of Clustered Federated Learning“
  Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020