The Uncertainty in Complex Systems lab aims to identify the mechanisms by which complex (networked) systems evolve and behave. Our main objectives are to identify the low-dimensional regularities in these systems so that we can interpret their behaviour, to predict how they will act in their future environment, and to exert control over them.
Of course, doing so is a daunting task. Not only are large-scale system dynamics difficult to identify in general, but our observations of these systems are often sparse, indirect and noisy. Dealing with both this epistemic and aleatoric uncertainty requires the development of new probabilistic models and techniques, which plays a central role in our group.
The output of our group is both fundamental as well as practical, with applications in several domains, including network neuroscience, genetics, (human) learning behaviour, and (personalized) healthcare.
All stable processes we shall predict. All unstable processes we shall control.
—John von Neumann