Joost-Pieter Katoen

A novel deductive verification framework for weighted programming reveals how to express and automate verification conditions for complex quantitative models.

Achieving optimal outcomes in probabilistic programs is now possible with a novel framework that synthesizes strategies across multiple objectives simultaneously.

Quantum programs can be analyzed for expected runtime without needing upper bounds, transforming how we approach verification in this domain.

Storm's decade-long development now offers a powerful, Python-integrated environment for probabilistic model checking, making it easier than ever to analyze Markov decision processes and models with uncertainty.

A single program transformation unlocks a unified approach to analyze diverse objectives on probabilistic programs, including higher moments and threshold probabilities, using existing wp-reasoning techniques.