Adaptive Models & Big Data

Models help understand and predict a large number of natural phenomena, from climate change and extreme events, to metabolism of organisms, or cosmic processes (formation and evolution of galaxies and the cosmic web, black hole formation and growth, star formation). The availability of computing resources, large amounts of data, and the continuous monitoring of complex systems alter traditional views on the nature, role, adaptation and refinement of models. Models have thus become an interesting object of research in themselves.

The DSSC approaches models by connecting data and complexity science: research at the Centre hinges on the combination of statistics and computer science in statistical machine learning, and on the preoccupation with explanatory models in statistics. The DSSC also assumes complex systems modelling as a research focus, based on its potential to increase the effective use of modelling and to share insights over a wide range of traditional disciplines. The specialized modelling efforts in each traditional discipline yields fundamental knowledge that can potentially be applied to problems in other, sometimes very remote, disciplines.

Within this topic, the DSSC focuses on system reconstruction, learning of models and large scale computing.

Relevant projects in this area can be found here.