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October 29, 2021

Award for paper on processing semantic data streams

Congratulations to BIFOLD Fellow Prof. Dr. Manfred Hauswirth and BIFOLD Junior Fellow Dr. Danh Le-Phuoc: At the International Semantic Web Conference 2021 (ISWC-2021), their paper “A Native and Adaptive Approach for Unified Processing of Linked Streams and Linked Data” achieved 2nd place for the “SWSA Ten-Year Award” with an honourable mention.

© Unsplash
The Berlin Cell Hospital brings together experts from clinical practice, biomedical research, technology, data science, mathematics and engineering science.
October 25, 2021

New Berlin Cell Hospital announced

On October 13, 2021, at an event celebrating the 200th birthday of the famous pathologist, physician and socialist politician Rudolf Virchow, the Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC) and the Charité – Universitätsmedizin Berlin, together with the Berlin Institute for the Foundations of Learning and Data (BIFOLD) and several other Berlin research institutions declared the founding of the Berlin Cell Hospital. The new Cell Hospital wants to shape and develop the cell-based medicine of the future.

At first glance, no manipulation is visible in the input image. However, after scaling down, the output is past all recognition.
October 07, 2021

Preventing Image-Scaling attacks on Machine Learning

BIFOLD Fellow Prof. Dr. Konrad Rieck, head of the Institute of System Security at TU Braunschweig, and his colleagues provide the first comprehensive analysis of image-scaling attacks on machine learning, including a root-cause analysis and effective defenses. Konrad Rieck and his team could show that attacks on scaling algorithms like those used in pre-processing for machine learning (ML) can manipulate images unnoticeably, change their content after downscaling and create unexpected and arbitrary image outputs. The work was presented at the USENIX Security Symposium 2020.

© Pixabay
BIFOLD researchers suggest a new machine learning model that leads to both: high accuracy and fairness.
September 21, 2021

In search for algorithmic fairness

Artificial intelligence (AI) has found its way into many work routines – be it the development of hiring procedures, the granting of loans, or even law enforcement. However, the machine learning (ML) systems behind these procedures repeatedly attract attention by distorting results or even discriminating against people on the basis of gender or race. “Accuracy is one essential factor of machine learning models, but fairness and robustness are at least as important,” knows Felix Neutatz, a BIFOLD doctoral student in the group of Prof. Dr. Ziawasch Abedjan, BIFOLD researcher and former professor at TU Berlin who recently moved to Leibniz Universität Hannover. Together with Ricardo Salazar Diaz they published “Automated Feature Engineering for Algorithmic Fairness“, a paper on fairness of machine learning models in Proceedings of the VLDB Endowment.

© Unsplash
The new type of algorithm may help to understand which brain regions directly interact with one another.
September 07, 2021

New type of algorithm for brain research

Together with an international team of researchers from Mayo Clinic BIFOLD Co-Director Prof. Dr. Klaus-Robert Müller developed a new type of algorithm to explore which regions of the brain interact with each other. Their results could improve brain stimulation devices to treat disease. For millions of people with epilepsy and movement disorders such as Parkinson’s disease, electrical stimulation of the brain already is widening treatment possibilities. In the future, electrical stimulation may help people with psychiatric illness and direct brain injuries, such as stroke.

© Unsplash / Fusion Medical Information
Visualization of the SARS-CoV-2 virus.
September 06, 2021

Using Machine Learning in the fight against COVID-19

BIFOLD Fellow Prof. Dr. Frank Noé identified a potential drug candidate for the therapy of COVID-19. Among other methods, they used deep learning models and molecular dynamics simulations in order to identify the drug Otamixaban as a potential inhibitor of the human target enzyme which is required by SARS-CoV-2 in order to enter into lung cells. According to their findings, Otamixaban works in synergy with other drugs such as Camostat and Nafamostat and may present an effective early treatment option for COVID-19. Their work was now published in Chemical Science.

© Petros Gigis et al.
Percentages of internet users that can be served by Hypergiants’ off-nets in their networks.
August 26, 2021

SIGCOMM 2021Best Paper: 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.

© BOSS Workshop organizers
August 16, 2021

VLDB2021: BOSS Workshop features Open Source Big Data systems

BIFOLD researchers will present three full research papers as well as three demo papers at the 47th International Conference on Very Large Data Bases (VLDB 2021), which will take place from August 16 – 29, 2021. In conjunction with VLDB, BIFOLD researchers also co-organize the BOSS 2021 workshop on open source big data systems.

© European Space Agency
Visualization of sea surface temperature and salinity based on EO data.
August 10, 2021

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.

© BIFOLD
LTR: Dr. Kaustubh Beedkar, Dr. Jan Hermann, Dr. Marina Marie-Claire Höhne, Dr. Danh Le Phuoc, Dr. Kristof Schütt, Dr. Eleni Tzirita Zacharatou
July 22, 2021

BIFOLD welcomes the first six Junior Fellows

The Berlin Institute for the Foundations of Learning and Data is very pleased to announce the first six BIFOLD Junior Fellows. They were selected for the excellence of their research and are already well-established researchers in the computer sciences. In addition, their research interests show exceptional potential for BIFOLD’s research goals, either by combining machine learning and data management or by bridging the two disciplines and other research areas. The first six Junior Fellows will cover a broad range of research topics during their collaboration with BIFOLD.