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Official announcement of BIFOLD

Photo: TU Berlin / Felix Noak

Photo: TU Berlin / Felix Noak

Photo: TU Berlin / Felix Noak

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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

  • 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
  • Del Monte, Bonaventura; Zeuch, Steffen; Rabl, Tilmann; Markl, Volker:
    „Rhino: Efficient Management of Very Large Distributed State for Stream Processing Engines“
    accepted at ACM SIGMOD/PODS International Conference on the Management of Data, 2020
  • Derakhshan, Behrouz; Mahdiraji, Alireza Rezaei; Abedjan, Ziawasch; Rabl, Tilmann; Markl, Volker:
    „Optimizing Machine Learning Workloads in Collaborative Environments“
    accepted at ACM SIGMOD/PODS International Conference on the Management of Data, 2020
  • Grulich, Philipp M.; Breß, Sebastian; Zeuch, Steffen; Traub, Jonas; von Bleichert, Janis; Chen, Zongxiong; Rabl, Tilmann; Markl, Volker:
    „Grizzly: Efficient Stream Processing Through Adaptive Query Compilation“
    accepted at ACM SIGMOD/PODS International Conference on the Management of Data, 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
  • Wiedemann, Simon; Kirchhoffer, Heiner; Matlage, Stefan; Haase, Paul; Marban, Arturo; Marinc, Talmaj; Neumann, David; Nguyen, Tung; Osman, Ahmed; Schwarz, Heiko; Marpe, Detlev; Wiegand, Thomas; Samek, Wojciech:
    „DeepCABAC: A Universal Compression Algorithm for Deep Neural Networks“
    IEEE Journal of Selected Topics in Signal Processing, 14(3):1-15, 2020
  • Strodthoff, Nils; Wagner, Patrick; Wenzel, Markus; Samek, Wojciech:
    „Universal Deep Sequence Models for Protein Classification“
    Bioinformatics, btaa003, 2020
  • Samek, Wojciech:
    „Learning with Explainable Trees“
    Nature Machine Intelligence, 2:16-17, 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
  • Redyuk, S.; Schelter, S.; Rukat, T.; Markl, V.; F. Biessmann, F.: „Learning to Validate the Predictions of Black Box Machine Learning Models on Unseen Data“
    HILDA’19, Amsterdam, Netherlands, 2019