J. 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.

Adversarially fine-tuning a CNN for time-series classification in a high-stakes physics application (CERN collimator alignment) boosts robustness by nearly 20% without hurting clean accuracy.

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.