Berlin Institute for the Foundations of Learning and Data (BIFOLD)

The Berlin Institute for the Foundations of Learning and Data (BIFOLD) conducts research on the foundations of Big Data and Machine Learning. For this, BIFOLD brings together several institutions and fosters their collaboration. The involved research groups are conducting research on a wide range of topics in order to yield solutions that have a considerable positive impact for society, the economy, and science.

Our team conducts research on adaptive resource management as a means towards easy-to-use, yet also efficient and dependable distributed data-parallel processing. That is, we are generally passionate about systems and tools that automatically adapt to changing workloads and computing environments. In BIFOLD, we are specifically looking at adaptive monitoring, fault tolerance, and scheduling of continuous processing jobs in context of large-scale IoT systems.  For this, we are developing and evaluating new methods for the adaptive usage of heterogeneous, distributed resources for the efficient processing of sensor data streams. The goal is to capture the key characteristics of constantly changing computing environments and workloads and have distributed processing systems adjust appropriately to major changes, so the required quality of service can be provided as far as possible and even in case of failures.

Institutional Partners of BIFOLD:

• TU Berlin
• FU Berlin
• HU Berlin
• Charité Berlin
• University of Potsdam
• TU Braunschweig
• Fraunhofer HHI
• Fraunhofer Fokus
• German Research Center for Artificial Intelligence (DFKI)
• Zuse Institute Berlin (ZIB)
• Deutsches Herzzentrum Berlin (DHZB)
• Max-Delbrück-Centrum für molekulare Medizin (MDC)
• Weierstrass Institute for Applied Analysis and Stochastics (WIAS)
• Hasso Plattner Institut (HPI)
• Max-Planck-Institut für Wissenschaftsgeschichte (MPIWG)
• Max-Planck-Institut für molekulare Genetik (MPIMG)
• Max-Planck-Institut Informatik (MPI-INF)

• Funding: Federal Ministry of Education and Research
• Website: https://bifold.berlin/ [1]