Research areas
Many of the greatest mathematical and computational challenges faced in almost any science today stem from the combination of large amounts of data and the complexity of the systems under study. In that sense, big data and complex systems go hand in hand, and can be considered two sides of the same coin. Indeed, large quantities of data by themselves do not provide much of a challenge if the underlying system producing these data is simple enough. By contrast, even modest amounts of data can cause insurmountable computational and mathematical problems if the best algorithms or models have high complexity. When complex systems and big data collide, both challenges and opportunities multiply.
A research cluster in data science and systems complexity allows for a helicopter-view approach to these problems, connecting the seemingly distant modelling approaches in science at a high level. Fields such as astronomy, physics, bioinformatics, or medicine generate big data sets and provide extensive experience in data acquisition, storage, and management. Mathematics, statistics, computer science, and engineering develop generic methodologies for modelling, computation, analysis, and system design. Input from the applied economic, biological, health, or social sciences is indispensable to derive meaningful output in these various domains.
The research DSSC pursues three main research lines:
Last modified: | 28 April 2021 4.39 p.m. |