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.
Following an announcement of the WHO, who declared the coronavirus a global pandemic, governments around the world began enacting stay-at-home orders, regulations for working from home and homeschooling. Within a single week, Internet traffic volume increased by 25 percent – an increase which under normal circumstances is usually observed over the course of a year. Taking account of increased use during the second lockdown in fall 2020, the overall use of Internet services in 2020 increased between 35 and 50 percent, depending on the network. An international, interdisciplinary group of researchers led by Professor Dr. Georgios Smaragdakis, professor of Internet measurement and analysis at TU Berlin and Fellow of the Berlin Institute for the Foundations of Learning and Data (BIFOLD), has published these figures and other findings in a paper in Communications of the Association for Computing Machinery (ACM). The leading professional association recently named the paper a research highlight.
The upcoming 2021 ACM International Conference on the Management of Data (SIGMOD) – a top ranked international conference on database systems and information management – accepted seven papers submitted by BIFOLD Researchers. Large amounts of high-quality data are the backbone of modern machine learning applications in research, industry, and sectors, like medicine and mobility. To enable the next generation of Artificial Intelligence applications, an increasing number of different data sources need to be accessed and analyzed in a shorter period of time, while reducing computation costs, maintaining fault tolerance, and achieving high data quality. The group of BIFOLD Researchers, led by BIFOLD Co-Director Prof. Dr. Volker Markl, tackled some of these data management challenges and developed innovative solutions.
A future-proof IT infrastructure is increasingly becoming a decisive competitive factor – this applies not only to companies, but especially to research. In recent months, BIFOLD has been able to invest around 1.8 million euros in new research hardware, thereby significantly increasing the institute’s computing capacity. This cutting-edge IT infrastructure was financed by the German Federal Ministry of Education and Research (BMBF).
The Berlin Institute for the Foundations of Learning and Data (BIFOLD) set up two new Research Training Groups, led by Dr. Stefan Chmiela and Dr. Steffen Zeuch. The goal of these new research units at BIFOLD is to enable a junior researcher to conduct independent research and prepare him for a leadership position. Initial funding includes their own position as well as two PhD students and/or research associates for three years.
In time for the summer semester 2021, the Berlin Institute for the Foundations of Learning and Data (BIFOLD) announced the launch of its Graduate School (GS): 12 PhD students from France, Russia and Germany, among them four women, make up the first cohort. The scholarship holders have obtained their master’s degrees in physics, computer science or bioinformatics; two of them are currently researching at Freie Universität Berlin, one at Universität Potsdam and nine at Technische Universität Berlin.
Klaus-Robert Müller, professor of machine learning at TU Berlin and Co-Director of the Berlin Institute for the Foundations of Learning and Data (BIFOLD), discusses computation time as a climate killer and his predictions for science in 80 years.
The 37. IEEE International Conference on Data Engineering (ICDE) 2021 honored the paper “Efficient Control Flow in Dataflow Systems: When Ease-of-Use Meets High Performance” of six BIFOLD researchers with the Best Paper Award. Gábor E. Gévay, Tilmann Rabl, Sebastian Breß, Lorand Madai-Tahy, Jorge-Arnulfo Quiané-Ruiz and Volker Markl were honored during the award session of the conference on April 21, 2021.
The research paper “Fast CSV Loading Using GPUs and RDMA for In-Memory Data Processing” by Alexander Kumaigorodski, Clemens Lutz, and Volker Markl received the Best Paper Award of the 19th Symposium on Database Systems for Business, Technology and Web (BTW 2021). On top, the paper received the Reproducibility Badge, awarded for the first time by BTW 2021, for the high reproducibility of its results.
Electroencephalography (EEG), electrocardiography (ECG), electromyography (EMG) – all of these non-invasive medical diagnostic methods rely on an electrode to measure and record electrical signals or voltage fluctuations of muscle or nerve cells underneath the skin. Depending on the type of diagnostics, this can then be used to measure electrical brain waves, or the currents in the heart or muscles. Present methods use metal sensors which are attached to the skin using a special gel to ensure continuous contact. Researchers at the University of Korea and Technische Universität Berlin have now developed so-called biosensors made of the plant material cellulose. They not only offer better and more durable conductivity than conventional electrodes. They are also 100 percent natural, reusable, do not cause skin irritation like other gels and are biodegradable. The paper “Leaf inspired homeostatic cellulose biosensors” has now been published in the renowned journal Science Advances.