Hyperproperties capture system requirements that relate multiple execution traces at once.
Our research sets out to develop a unified theory for general hyperproperties. This includes the development of new logics that can capture rich classes of hyperproperties, including information flow policies, causality, and algorithmic fairness.
Reactive synthesis automatically constructs a system that continuously responds to its environment while guaranteeing that a formal specification is always satisfied.
Our research targets the synthesis of distributed systems with multiple components and cyber-physical systems that combine discrete controllers with models of the dynamics of their physical environment. We specifically aim for synthesis techniques that produce understandable, trustworthy solutions.
Runtime monitoring observes a system during execution to detect property violations, security breaches, or anomalous behavior as they occur.
In our research, we develop formal specification languages for monitoring, algorithms for the synthesis of resource-efficient and correct-by-construction monitors, and domain-specific solutions for autonomous cyber-physical systems. At the center of our research is the stream-based runtime monitoring framework RTLola.
Neuro-symbolic computing integrates learning-based neural methods with symbolic reasoning to create AI systems that are both data-driven and logically grounded.
In our research, we use deep learning methods to facilitate the formalization, formal verification and synthesis of software and hardware systems. The goals of this research include developing methods with both neural and algorithmic components that increase the applicability and reduce the manual effort of formal methods.
