Bern Data Science Initiative
Bern Data Science Initiative
Research Domains and Groups
Digital Humanities, Prof. Dr. Tobias Hodel, "Digital Humanities combines the Humanities with methods of Computer Science. At the University of Bern, Digital Humanities focuses on approaches of machine and deep learning applied on Early Modern texts and languages. We currently train and use machine learning models for Named Entity Recognition and part-of-speech as well as distant reading approaches that improve the accessibility to large document corpora.”
Mineralogy, Prof. Dr. Sergey Churakov, "With the exponential growth of computational resources, data storage capacities and opportunities of fast information exchange, the emerging «forth generation» scientific paradigm of big data and modelling driven research unifies the most advanced achievements of empirical science, theoretical model development as well as computer experiments and opens horizons for new scientific discoveries."
Laboratory for High Energy Physics. Prof. Dr. Michele Weber. "LHEP is involved in detector hardware, operation, data processing and the physics analysis of data. The amounts of data coming out of the particle detectors at CERN in Geneva have from the beginning been pushed to the limit of the data processing and data science capabilities available."
Institute of Cell Biology, Prof. Dr. Olivier Pertz. "The use of high content, automated microscopy now allows us to analyse biological systems to a throughput not accessible before. The huge imaging data we generate cannot be analysed by humans anymore, but require modern computational techniques such as computer vision. The BeDSI has been instrumental in setting up the high performance computing infrastructure in our lab that allows this to happen."
Mathematical Institute, Prof. Dr. Christiane Tretter. " Mathematics is the basis of all present and future key technologies. In the age of machine learning and artificial intelligence, fundamental research in mathematics and academic education of the next generation in algorithmic thinking and complex problem solving is more important than ever."
Science IT Support, Prof. Dr. Christiane Tretter, PD Dr. Sigve Haug. "Data science support and research are becoming competitive prerequisuites in more and more domains."
Computer Science, Prof. Dr. Paolo Favaro. "We are interested in building useful feature representations of images. In our approaches a good representation is one that makes future learning easier."
Chemistry, Prof. Dr. Jean-Louis Reymond. "We are using cheminformatics and artificial intelligence to enumerate all theoretically possible organic molecules, which together form the chemical space. We also develop specific tools to visualize and search the resulting very large multidimensional databases. Our goal is to identify innovative molecules with predicted biological activities, which we then synthesize and test in the laboratory as potential new drugs.”
Statistics, Prof. Dr. David Ginsbourger. "Challenges from science and society are increasingly tackled by combining data and models arising from heterogeneous sources and disciplinary fields, all coming with their lots of uncertainties. Statistics offers a broad platform where resulting problems can be formulated abstractly and ultimately adressed by leveraging elegant theories, efficient computations, and interpretable outcomes.”
Communication and Distributed Systems / Computer Science, Prof. Dr. Torsten Braun „We are working on how to support distributed applications such as user mobility prediction in mobile networks and Internet of Things by federated machine learning in order to support privacy and scalability."
Climatology, Prof. Dr. Stefan Broennimann. "The Climatology Group focuses on documenting and understanding past and present variations in weather and climate by bringing together historical climate data with numerical simulations using data assimilation techniques."
mLAB, Prof. Dr. Susan Thieme, Institute of Geography. "What role can contemporary digital media practices play in collecting, analyzing and communicating data? We encourage researchers to develop modes of collaborative work and to critically use arts, media and digital research methods as an integral part of their work."
Economic Geography, Prof. Dr. Heike Mayer. "Digital technologies have become a central part of our everyday lives. The possible "multi-locality" of certain types of work tasks via cloud software and Internet applications fosters new urban-rural linkages. The analysis of changing geographic patterns of work due to the use of digital technologies is at the focus of the SNF funded project Digital Lives".
Biochemistry, Prof. Dr. Sebastian Leidel. "The rapid increase of different types of Omics data in biomedical research forces us to handle our questions and analyses in novel ways. Only if we are able to integrate such datasets in meaningful ways, will we be able to yield the promises of novel technologies. It is critical to learn from other fields and how they handle such challenges."
Mathematical Institute, Prof. Dr. Jan Draisma. "High-dimensional data arrays can often be approximated by low-dimensional structures. The mathematics of tensor decomposition aims to find such approximations. In our research, we develop the theory of tensor decomposition and its applications to graphical statistical models used in data science."
Statistics, Prof. Dr. Johanna F. Ziegel. “Statistics provides theoretical foundations of how to best analyze data or retrieve information from data. We are keen to take on the challenge to provide solid foundations and new methodology for increasingly complex data sets in many application areas."
ARTORG and MIA Lab, Prof. Dr. Mauricio Reyes. "Artificial Intelligence is advancing medical technologies and healthcare to the next level. Developing interpretability technologies is essential to enhance trustability and reliability of these technologies."
Philosophy of Science, Prof. Dr. Dr. Claus Beisbart. "While deep learning algorithms are successfully applied in many areas, they are not yet well understood... To contribute to a better understanding of deep learning [we carry] out interdisciplinary research at the borderline between computer science and philosophy."
ARTORG, CAIM, Prof. Dr. Raphael Snitzman. "Essentially, in AI we take data and build as coherent a model of the world as possible. This journey of going from data to a compact representation that allows us to make predictions is what drove my curiosity from the very start."
Microscopy Imaging Center (MIC), Prof. Dr. Britta Engelhardt (chair), Prof. Dr. Ruth Lyck (coordinator). "MIC Supports and coordinates microscopy facilities with big data in the Medical Faculty, the Faculty of Science and the Vestsuisse Faculty.”
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Bern Data Science Initiative