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About

Calum Imrie is a researcher working at the intersection of autonomous systems, formal methods, and normative requirements engineering. His work addresses some of the most pressing challenges in deploying safe and trustworthy AI-driven systems in critical domains such as healthcare, transportation, space exploration, and infrastructure inspection. Imrie has made significant contributions to the synthesis of correct-by-construction controllers for autonomous systems that incorporate deep neural network perception components, embodied in his DeepDECS framework, which has attracted 10–13 citations in its various forms. His research on Bayesian learning for robust robot verification (11 citations) demonstrates a rigorous probabilistic approach to ensuring autonomous agents can safely complete missions in dynamic, uncertain environments. He has also advanced the formal analysis of normative non-functional requirements, developing satisfiability-based techniques for identifying and resolving conflicts in social, legal, ethical, and cultural norms governing software systems (13 citations). Earlier work explored brain-inspired deep recurrent neural network architectures for robotic control and swarm robotics, reflecting a broad and evolving research trajectory. Across his publications, Imrie consistently bridges theoretical rigor with practical applicability, positioning him as a valuable contributor to the growing field of safe and explainable autonomous systems.