Responsible AI» more info
Foundations & Methods» more info
Management of Data Science Processes» more info
Architectures & Technologies» more info
Systems & Tools» more info
AI robot excels in Olympic sport

A Deep Reinforced Learning framework, developed by BIFOLD Co-director Prof. Dr. Klaus-Robert Müller and his colleagues at Korea University, enabled the robot “Curly” to beat top-level athletes in the Olympic sport of curling.

© Korea University

© Korea University

© Korea University

Official announcement of BIFOLD

Photo: TU Berlin / Felix Noak

Photo: TU Berlin / Felix Noak

Photo: TU Berlin / Felix Noak

previous arrow
next arrow
Slider
Upcoming Events
19
Oct2020
„Scalable Machine Learning on Large Sequence Collections“Research talk by Themis Palpanas (French University Institute)
16:00 – 16:45 CETVirtual Event
03 – 05
Nov2020
European Big Data Value ForumKeynotes by BIFOLD Director Prof. Dr. Markl and Principal Investigator Prof. Dr. Wiegand
Virtual Event
Latest News

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

  • 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
  • Said Jawad Saidi, Anna Maria Mandalari, Roman Kolcun, Hamed Haddadi, Daniel J. Dubois, David Choffnes, Georgios Smaragdakis, and Anja Feldmann:
    „A Haystack Full of Needles: Scalable Detection of IoT Devices in the Wild“
    ACM IMC 2020
  • John P. Rula, Philipp Richter, Georgios Smaragdakis, and Arthur Berger:
    „Who’s left behind? Measuring Adoption of Application Updates at Scale“
    ACM IMC 2020
  • Srdjan Matic, Costas Iordanou, Georgios Smaragdakis, and Nikolaos Laoutaris:
    „Identifying Sensitive URLs at Web-Scale“
    ACM IMC 2020
  • Le-Tuan Anh; Hayes, Conor; Hauswirth, Manfred; Le-Phuoc, Danh:
    „Pushing the Scalability of RDF Engines on IoT Edge Devices.“
    Sensors 2020, no. 10: 2788, 2020
  • Nguyen Duc-M., Le-Tuan A., Calbimonte JP., Hauswirth M., Le-Phuoc D.:
    „Autonomous RDF Stream Processing for IoT Edge Devices.“
    InSemantic Technology. JIST 2019. Lecture Notes in Computer Science, vol 12032. Springer, Cham.
  • Felix Sattler, Klaus-Robert Müller, and Wojciech Samek:
    „Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints“
    IEEE Transactions on Neural Networks and Learning Systems, 2020