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About

Jane Hillston is a distinguished computer scientist whose research sits at the intersection of formal methods, performance modelling, and complex adaptive systems. She is perhaps best known for her pioneering work in stochastic process algebra, a mathematical framework that elegantly extends classical process algebra to enable both qualitative and quantitative analysis of dynamic systems. Her 1996 paper on stochastic process algebra applied to robot control problems — her most cited work with 26 citations — demonstrated how this integrated approach could illuminate the behaviour of unreliable systems, opening new pathways for rigorous performance analysis in engineering contexts. In later years, Hillston turned her attention to collective adaptive systems (CAS), a challenging research frontier encompassing everything from animal swarms to sensor networks and human-ICT hybrid systems. Her contributions to the CARMA modelling language represent a significant step forward, providing high-level quantitative tools for designing and analysing systems where distributed agents pursue goals under resource constraints. Her repeated engagement with the fundamental challenges of CAS analysis underscores her role as a thought leader in this emerging area. For students exploring formal modelling, performance analysis, or complex systems design, Hillston's body of work offers both foundational theory and forward-looking methodology.