Data Science and Systems Complexity (DSSC)

The Faculty of Science and Engineering has identified Data Science and Systems Complexity as a core research theme. It concerns a research cluster of 58+ prominent senior researchers in a number of basic disciplines (mathematics, astronomy, computer science, artificial intelligence, systems & control) and scientific application domains (genomics, pharmacology, instrumentation). The ambition is to combine data science and complexity science. Simulations or measurements of complex systems, like climate models or coupled cell systems, give rise to (big) data in a mixed deterministic and random style. Knowledge on the origin of such a data facilitates the mathematical understanding of it. Conversely, given a data set, one likes to construct a model, usually complex, from which the observed data may emerge. Methodologies from statistics, nonlinear dynamics (e.g., time series analysis) and control theory (e.g., system identification) are particularly useful - even in early stages of analysis where an appropriate model cannot be completely determined. The challenge is to apply, adapt and develop techniques for understanding complex systems that generate big data. This fundamental knowledge allows for knowledge discovery in big data and for decision that is based on a fundamental understanding of complexity itself.