Uncertainty is nowadays more and more pervasive in computer science. It is important both in big data and at the level of events and control. Applications have to treat large amounts of data, often from unreliable sources such as noisy sensors and untrusted web pages. Data may also be subject to continuous changes, may come in different formats, and is often incomplete. Robots, trains, and production machines have to deal with unpredictable environments. The growing use of machine-learning components — often providing weak guarantees — forms an additional factor of uncertainty.

The aim of this RTG is to significantly advance various theoretical concepts (in algorithms, logic, verification) as well as their connection to deal with uncertainty and randomness, and to tailor and apply these techniques to problems in application areas such as railway engineering, network dynamics, and cyber-physical systems

Creative Commons BY 3.0 DE

Joost-Pieter Katoen