Rate My Professor Simon Burton

Professor Simon Burton holds the Chair in Systems Safety in the Department of Computer Science at the University of York, where he also serves as Business Lead for the Centre for Assuring Autonomy. He earned a BEng in Computer Science from the University of York in 1996 and a DPhil in Computer Science from the same institution in 2001, with his doctoral research focused on the automated generation of high integrity test suites from graphical specifications. Early in his career, Burton developed telecommunications software and worked as a research associate in the High Integrity Systems Engineering Group at the University of York. He subsequently managed research, development, and consulting organizations in industry, including positions at DaimlerChrysler (Mercedes) and Bosch. Prior to his current role, he served as Scientific Director for Safety Assurance at the Fraunhofer Institute for Cognitive Systems IKS.

Burton's research explores the intersection of systems safety engineering, artificial intelligence, and the legal, ethical, and regulatory considerations essential for forming safety assurance arguments in complex, autonomous, and AI-based systems. Central questions in his work include defining application-specific notions of 'safe enough,' engineering safe autonomous systems through AI, machine learning, safety analysis, and systems engineering methods, and constructing defensible arguments for achieved safety levels using evidence from analysis, testing, and belief theory. In the automotive domain, his efforts address the safe integration of AI for perception and planning in automated vehicles. He is convenor of the ISO working group TC22/SC32/WG14 on safety and AI for road vehicles and led the development of ISO PAS 8800, the first standard in this area. Key publications include 'Mind the Gaps: Assuring the Safety of Autonomous Systems from an Engineering, Ethical, and Legal Perspective' (2019), 'Confidence Arguments for Evidence of Performance in Machine Learning for Highly Automated Driving Functions' (2019), 'Effective and Reflective Assurance for AI-based Autonomy' (2026), and 'A Case Study on Defining Traceable Machine Learning Safety Requirements for an Automotive Perception Component' (2025). Burton collaborates with multinational companies in automotive, industrial robotics, maritime, and healthcare sectors, providing training, regulatory support, and research program assistance.