The goal of our research group is to develop theoretical foundations and techniques for the construction of safe, reliable, and secure autonomous cyber-physical systems. Our work spans from formal specification, verification, synthesis, to runtime verification.
Alejandro presenting our work on Learning Contextual Runtime Monitors for Safe AI-Based Autonomy at the I Encuentro Estalmat in Malaga, Spain
This is joint work with UC Berkeley
Antonina presenting the work on Learning Robust Markov Models for Safe Runtime Monitoring at AAMAS 2026 in Paphos, Cyprus
This is joint work with Radboud University
Our paper on Learning Contextual Runtime Monitors for Safe AI-Based Autonomy got the Best Paper Award for the Learning-Enabled Autonomy track at ICCPS 26
This is joint work with UC Berkeley
Our paper on Learning Robust Markov Models for Safe Runtime Monitoring has been accepted for presentation at AAMAS 26
This is joint work with Radboud University Nijmegen
Our paper on Learning Contextual Runtime Monitors for Safe AI-Based Autonomy has been accepted for presentation at ICCPS 26
This is joint work with UC Berkeley
STARlab presenting ODD monitor generation using Scenic at the International Conference on Runtime Verification
Andrzej Rzeczycki joining STARlab in October as a WASP PhD Student.
Welcome to the team Andrzej!
Alasdair Paren joining STARlab in October as a WASP Postdoc.
Welcome to the team Alasdair!
Mengyuan Wang joining STARlab as a WASP PhD Student.
Welcome to the team Mengyuan!
The Runtime Verification Conference of 2025 will take place on September 15th to 19th, in Graz, Austria.
Co-Chairs: Bettina Könighofer and Hazem Torfah
Join us in Graz!
