Research themes at the Risk Institute

Our research themes each span all disciplines within the Centre. In this way the thematic structure is designed to stimulate cross-disciplinary research and training. The interconnections among the themes require the cohort-based approach, where students with backgrounds in different disciplines work cooperatively and learn from each other.

Data

DataMeasurement and uncertainty characterisation

A basic question is how to make proper use of the statistical uncertainty in measurements collected in new or fluctuating environments. The issue is complicated when measurements must be obtained under inhospitable conditions or at great expense. Imprecision can be important even in ‘big data’ cases such as remote sensing imagery.

Data

ModelsSimulation methods and multi-scales extrapolation

Sophisticated techniques are required to weave together available data and often incomplete scientific knowledge about the underlying physical processes into models that can be used to forecast risks at scales relevant to practical decision making. Methods are needed to simulate rare events and to evaluate systems and design under uncertainty for all kinds of models from simple arithmetic expressions to complex finite-element or differential-equation codes.

Data

CommunicationCommunication under risk and uncertainty

Humans are known to have substantial biases in their perception of risks and uncertainties. Lay people and professionals alike share these biases. Some special techniques are available to overcome the problems created, but further research is keenly needed in this area, especially for communicating with the public.

Data

Decision MakingDecision making in complex systems and environements

Sophisticated techniques are required to weave together available data and often incomplete scientific knowledge about the underlying physical processes into models that can be used to forecast risks at scales relevant to practical decision making. Methods are needed to simulate rare events and to evaluate systems and design under uncertainty for all kinds of models from simple arithmetic expressions to complex finite-element or differential-equation codes.