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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.
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.
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.
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.
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.
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.
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 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.
Higher impact through reproducibility
Modern science is based on objectiveness. Experimental results should be repeatable by any scientist, provided they use the same experimental setup. Since 2008, the SIGMOD conference, the international leading conference in management of data, awards the reproducibility badge to signify that a scientific work has been successfully reproduced by a third-party reviewer. In 2021, the paper “Pump Up the Volume: Processing Large Data on GPUs with Fast Interconnects” by BIFOLD researcher Clemens Lutz was awarded a prestigious reproducibility badge.
In search of Europe’s scientific identity
In the past, scholars used to pore over dusty tomes. Today Dr. Matteo Valleriani, group leader at the Max Planck Institute for the History of Science as well as honorary professor at TU Berlin and fellow at the Berlin Institute for the Foundations of Learning and Data (BIFOLD), 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.