At the high-tech risk lab, we work on risks, risk-taking and resilience.
Leading in our work is better risk-taking: our society faces more and more complex risks, e.g., in drones, self-driving cars and hyperloops: How can we handle the complex interactions in these systems, align different stakeholders, and effectively integrate failure data and expert judgements?
Our overall ambition is to make decision-making on risks more accountable: systematic, so that no obvious risks are overlooked; transparent, so experts can discuss their standpoints; and quantitative, based on facts and figures, not on failing intuitions. These are essential building blocks for a resilient, well-functioning, and (economically) healthy society.
Our research focusses on methods and tools to support decision-making. Concretely, we develop methods to identify, prioritize risks, so that (cost-)effective measures can be devised to mitigate the top risks. Concrete topics include:
- Fault tree analysis
- Safety-security co-analysis
- Predictive maintenance & smart diagnostics
- Stochastic model checking & game theory
Our research spans the entire spectrum from fundamental to practical. (risk modeling, algorithms) (industry-funded research, complex case studies).
We advocate action research. Rather than first working on theory and then evaluation-second, we work from the start with stakeholders on solutions for challenging problems. The scientific gain is to generalize specific solutions into generic principles, and a thorough understanding of why certain principles work (or don’t).
Resources
We have a variety of projects and resources to help visualize and quantify risks.
- FFORT, the fault tree forrest, is a collection of existing Faut Tree, Attack Tree, and BDMP models. These can be used as examples for understanding how to use these models, what results they can generate, or to use as a benchmark set for automated model checking tools.
- PrimaVera has a number of Datasets related to prognostics and health monitoring.
- FTVisualisations contains multiple resources for learning how fault trees work, including (interactive) visual representations of small example models.
People
