Joost-Pieter Katoen

Paper accepted in ACM TOPLAS

The paper “Conditioning in Probabilistic Programming” by Federico Olmedo, Friedrich Gretz, Nils Jansen, Benjamin L. Kaminski, Joost-Pieter Katoen and Annabelle McIver has been accepted for the journal ACM Transactions on Programming Languages and Systems. The paper treats the semantic intricacies of introducing conditioning in a probabilistic programming language, its interference with non-determinacy, and divergence.

Best Paper Award at SRDS 2017

The paper entitled “Automated Fine Tuning of Probabilistic Self-Stabilizing Algorithms” by Saba Aflaki, Matthias Volk, Borzoo Bonakdarpour, Joost-Pieter Katoen and Arne Storjohann has been selected as Prof. C.V. Ramamoorthy Best Paper Award at the 36th IEEE Int. Symposium on Reliable Distributed Systems in Hongkong. The paper presents automated techniques to find the probability distribution that […]

Symposium ModelEd, TestEd, TrustEd

In honour of the 60th birthday of Ed Brinksma, we co-organize the symposium “ModelEd, TestEd, TrustEd” on 18 October 2017, in the Amphitheatre (Vrijhof), University of Twente, the Netherlands. The symposium will be a scientific event hosting a number of renowned speakers with whom Ed Brinksma has cooperated in the past. The day will culminate […]

Paper accepted at SRDS 2017

The paper entitled “Automated Fine Tuning of Probabilistic Self-Stabilizing Algorithms” by Saba Aflaki, Matthias Volk, Borzoo Bonakdarpour, Joost-Pieter Katoen and Arne Storjohann has been accepted at SRDS 2017 in Hongkong. The paper presents automated techniques to find the probability distribution that achieves minimum average recovery time for randomized distributed self-stabilizing algorithms.

Paper accepted at SafeComp 2017

The paper entitled “Model-Based Safety Analysis of Vehicle Guidance Systems” by Majdi Ghadhab, Sebastian Junges, Joost-Pieter Katoen, Matthias Kuntz and Matthias Volk, has been accepted at SafeComp 2017. The paper emerged from a co-operation with BMW and presents a model-based approach towards the safety analysis of vehicle guidance systems by comparing different mappings from functional […]

12 vacant Ph.D. positions

We are looking for enthusiastic and highly qualified doctoral researchers. 12 positions are available within the Research Training Group (RTG) UnRAVeL. The key emphasis of an RTG is on the qualification of doctoral researchers with a focused research program and a structured training strategy. UnRAVeL aims to significantly advance probabilistic modelling and analysis for uncertainty […]

Paper accepted in IEEE Trans. on Industrial Informatics

The paper “Fast Dynamic Fault Tree Analysis by Model Checking Techniques” by Matthias Volk, Sebastian Junges, and Joost-Pieter Katoen has been accepted to IEEE Transactions on Industrial Informatics. The paper presents a novel state-space generation technique for DFTs and combines this with a simple, though very effective, abstraction.

5 million euros for RTG UnRAVeL

The German Research Council (DFG) has granted our proposal to launch a Research Training Group on “Uncertainty and Randomness in Algorithms, Verification and Logic” (UnRAVeL). It funds 15 Ph.D. positions over a period of 4,5 years to work on challenging foundational research questions to treat uncertainty. The project involves 12 research groups from theoretical computer […]

Two CAV’17 papers accepted

The papers “A Storm is Coming: A Modern Probabilistic Model Checker” by Christian Dehnert, Sebastian Junges, Joost-Pieter Katoen and Matthias Volk and “Markov Automata with Multiple Objectives” by Tim Quatmann, Sebastian Junges and Joost-Pieter Katoen have been accepted at CAV 2017. The first paper presents the new and powerful probabilistic model checker storm, whereas the […]

LICS’17 paper accepted

The paper “A Weakest Pre-Expectation Semantics for Mixed-Sign Expectations” by Benjamin L. Kaminski and Joost-Pieter Katoen has been accepted to LICS 2017.  The paper presents a wp–style calculus for reasoning about the expected values of mixed–sign unbounded random variables after execution of a probabilistic program.