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Tracking Spooky Action at a Distance
The use of AI in classical sciences such as chemistry, physics, or mathematics remains largely uncharted territory. BIFOLD and Google researchers have now successfully developed an algorithm to precisely and efficiently predict the potential energy state of individual molecules using quantum mechanical data. Their findings offer entirely new opportunities for material scientists.

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Scheduling Computing Tasks can reduce Emission
To reduce the carbon footprint of cloud computing, researchers from the Berlin Institute for the Foundation of Learning and Data (BIFOLD) investigated the potential of shifting delay-tolerant compute workloads, such as batch processing and machine learning jobs, to times where energy can be expected to be green.
Red fiber glas cables are connected to a hub.

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Internet Hypergiants Expand into End-User Networks
BIFOLD Fellow Prof. Dr. Georgios Smaragdakis and his colleagues received the prestigious ACM SIGCOMM 2021 Best Paper Award for their research into the expansion of Hypergiant’s off-nets. They developed a methodology to measure how a few extremely large internet content providers deploy more and more servers in end-user networks over the last years. Their findings indicate changes in the structure of the internet, potentially impacting network end-user experience and neutrality regulations.
Earth with clouded skies and a satellite viewed from space.

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Earth Observation data for Climate Change Research
Many environmental reports are based on the analysis of satellite images. BIFOLD researchers are creating AgoraEO, an infrastructure for Earth Observation (EO) data that enables federated analysis across different platforms, making modern EO technology accessible to all scientists and society, thus promoting climate change innovation worldwide.
A historical painting shows a woman measuring a model of earth with a pair of compasses.

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In Search of Europe’s Scientific Identity
BIFOLD Fellow Prof. Matteo Valleriani uses algorithms to group and analyze digitized data from historical works. The term used to describe this process is computational history. One of the goals of Valleriani’s research is to unlock the mechanisms involved in the homogenization of cosmological knowledge in the context of studies in the history of science.
December 21, 2021
Lifting the curse of dimensionality for statistics in ML
A recent paper by BIFOLD researcher Dr. Robert A. Vandermeulen and his colleague Dr. Antoine Ledent provides the first solid theoretical foundations for applying low-rank methods to nonparametric density estimation. The paper was presented at NeurIPS 2021.
December 15, 2021
Benchmarking neural network explanations
BIFOLD researcher Dr. Wojciech Samek and his colleagues established an Open Source ground truth framework, that provides a selective, controlled and realistic testbed for the evaluation of neural network explanations.
December 13, 2021
Two BIFOLD Papers Ranked as ESI Highly Cited and Hot Papers
Two machine learning papers by BIFOLD researchers received the “Essential Science indicators” (ESI) “Highly Cited” and “Hot Papers” labels for their impact in the science community.
December 02, 2021
Learning about Population Health from Twitter Texts

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December 02, 2021
BIFOLD Researchers Honored with BBAW and acatech Memberships
At the “Einsteintag 2021” event on November 26, both BIFOLD Co-Director Volker Markl and BIFOLD Fellow Frank Noé were announced as new members of the Berlin-Brandenburg Academy of Sciences and Humanities.
November 23, 2021
Science and Startups launches AI Initiative
Since 2021, Science & Startups has been specifically strengthening research transfer in the field of Artificial Intelligence (AI). Now they officially launched their new focus programme: K.I.E.Z. (Künstliche Intelligenz Entrepreneurship Zentrum).

Mission Statement and Goals

BIFOLD conducts foundational research in big data management and machine learning, as well as its intersection, to educate future talent, and create high-impact knowledge exchange.

The Berlin Institute for the Foundations of Learning and Data (BIFOLD), has evolved in 2019 from the merger of two national Artificial Intelligence Competence Centers: the Berlin Big Data Center (BBDC) and the Berlin Center for Machine Learning (BZML). Embedded in the vibrant Berlin metropolitan area, BIFOLD provides an outstanding scientific environment and numerous collaboration opportunities for national and international researchers. BIFOLD offers a broad range of research topics as well as a platform for interdisciplinary research and knowledge exchange with the sciences and humanities, industry, startups and society.

Data management (DM) and machine learning (ML) are the scientific and technical pillars powering the current wave of innovation in artificial intelligence (AI); it is the efficient processing and intelligent analysis of very large, complex, heterogeneous data that has the potential to revolutionize and substantially improve our lives and societies. BIFOLD conducts scalable yet agile foundational AI research. Furthermore, it addresses the emerging challenges and requirements created by the rapidly growing importance of data management and machine learning in practically all areas, from medicine, industry, natural sciences, humanities, e-commerce, and media, to government and society.