Big Data (BD) and Machine Learning (ML) -together with decision-making- are the key pillars of Artificial Intelligence (AI). The analysis of very large and heterogeneous amounts of data has the potential to revolutionize many areas of our lives, from the sciences, production, transport and energy, to political and social processes. However, dealing with BD and ML requires highly specialized knowledge and infrastructure and poses many challenges: it requires computer skills, mathematical skills, and engineering skills. It further demands a societal discussion regarding its ethical implications. BD and ML will disrupt all levels of society and will inspire completely new applications fueling innovation and strengthening the economy. The impact is already evident today: we entered the 'fourth paradigm' in the sciences and the economy is talking about the next industrial revolution.

Research in ML and BD has been an integral part of research activities at ZIB for 10 years now. With recent success stories and the strong growth of international research activities, ML and BD are becoming a cornerstone of our research strategy. In the fall of 2019, a new Berlin-based Competence Center, the “Berlin Institute for the Foundations of Learning and Data” (BIFOLD), was founded that integrates the former Berlin Center for Machine Learning (BZML) and the Berlin Big Data Center (BBDC) into a new structure that is funded by the German federal ministry of research and education (BMBF). ZIB, having been part of both, BZML and BBDC, is part of the consortium supporting BIFOLD and will implement new research groups in this context. The main focus lies on data stream analysis, digital precision medicine, and learning dynamical laws from data.

Publications

2024
An interpretable data-driven prediction model to anticipate scoliosis in spinal muscular atrophy in the era of (gene-) therapies Scientific Reports, 14(11838), 2024 Tu-Lan Vu-Han, Rodrigo Bermudez Schettino, Claudia Weiß, Carsten Perka, Tobias Winkler, Vikram Sunkara, Matthias Pumberger BibTeX
DOI
BIFOLD
Chimeric U-Net – Modifying the standard U-Net towards Explainability Artificial Intelligence, 2024 Kenrick Schulze, Felix Peppert, Christof Schütte, Vikram Sunkara BibTeX
DOI
BIFOLD
2022
Efficient Riemannian Statistical Shape Analysis with Applications in Disease Assessment Doctoral thesis, Freie Universität Berlin, Christof Schütte, Christoph von Tycowicz (Advisors), 2022 Felix Ambellan PDF
BibTeX
DOI
URN
BIFOLD
On the Sufficient Condition for Solving the Gap-Filling Problem Using Deep Convolutional Neural Networks IEEE Transactions on Neural Networks and Learning Systems, 33(11), pp. 6194-6205, 2022 Felix Peppert, Max von Kleist, Christof Schütte, Vikram Sunkara BibTeX
DOI
BIFOLD
2021
Geodesic B-Score for Improved Assessment of Knee Osteoarthritis Proc. Information Processing in Medical Imaging (IPMI), pp. 177-188, Lecture Notes in Computer Science, 2021 (preprint available as ZIB-Report 21-09) Felix Ambellan, Stefan Zachow, Christoph von Tycowicz PDF (ZIB-Report)
BibTeX
arXiv
DOI
BIFOLD
2020
Using Blockchain for Tamper-Proof Broadcast Protocols Master's thesis, Humboldt-Universität zu Berlin, Alexander Reinefeld, Björn Scheuermann (Advisors), 2020 Jonas Spenger PDF
BibTeX
URN
BIFOLD
2019
Identifying lncRNA-mediated regulatory modules via ChIA-PET network analysis BMC Bioinformatics, 20(1471-2105), 2019 Denise Thiel, Natasa Djurdjevac Conrad, Evgenia Ntini, Ria Peschutter, Heike Siebert, Annalisa Marsico BibTeX
DOI
BIFOLD