In our digital world, data is produced on an unprecedented scale. Securing and evaluating this data opens up new possibilities for fundamental research, enabling data science to gain new knowledge and help to develop solutions to the important issues of today.
Data science is at the interface of data management and engineering sciences, statistics, machine learning, algorithms, data optimisation and data visualisation. Its methods and knowledge are used, inter alia, in social and economic sciences, medicine and environmental science.
Researchers at ETH Zurich are developing new approaches to data science and machine learning. They are involved with other institutions at interdisciplinary research centres; ETH Zurich and EPFL jointly operate the Swiss Data Science Center in Zurich and Lausanne, which brings the expertise of data specialists together and offers an interdisciplinary platform that enhances education and the transfer of knowledge. As part of this national initiative, for example, ETH Zurich trains Master’s students in data science.
At the Max Planck ETH Center for Learning Systems, ETH Zurich and the Max Planck Society investigate the basic mechanisms of complex systems and develop approaches for learning systems that process data in new ways. At the Zurich Information Security and Privacy Center (ZISC), ETH researchers work with leading industry partners to develop new approaches to secure information systems and data exchange. ETH Zurich is setting up the Citizen Science competence centre with the University of Zurich to involve citizens in data science.
ETH Zurich is expanding its infrastructure in order to carry out data-intensive research. In 2017, it has set up LeonhardOpen and LeonhardMed; these two storage and computer clusters are capable of examining huge amounts of data for patterns and contexts.
Another special computing infrastructure is the supercomputer at the Swiss National Supercomputing Centre CSCS in Lugano, which is part of ETH Zurich. The CSCS is developing both its supercomputing infrastructure towards the exascale performance class and its user lab for simulation-based science. This means that questions that previously would have taken months to solve, if at all, can now be processed by the supercomputer in a few days.