Biography:
Jane Hillston was appointed Professor of Quantitative Modelling in the School of Informatics at the University of Edinburgh in 2006, having joined the University as a Lecturer in Computer Science in 1995. She is currently Head of the School of Informatics. She is a Fellow of the Royal Society of Edinburgh and Member of Academia Europaea. She currently chairs the Executive Committee of the UK Computing Research Committee.
Jane Hillston’s research is concerned with formal approaches to modelling dynamic behaviour, particularly the use of stochastic process algebras for performance modelling and stochastic verification. The application of her modelling techniques have ranged from computer systems, to biological processes and transport systems. Her PhD dissertation was awarded the BCS/CPHC Distinguished Dissertation award in 1995 and she was the first recipient of the Roger Needham Award in 2005. She has published over 100 journal and conference papers and held several Research Council and European Commission grants.
She has a strong interest in promoting equality and diversity within Computer Science; she is a member of the Women’s Committee of the BCS Computing Academy and chaired the Women in Informatics Research and Education working group of Informatics Europe 2016—2018, and during that time instigated the Minerva Informatics Equality Award.

Formal Approaches to Quantitative Evaluation
Qualitative evaluation of computer systems seeks to ensure that the system does not exhibit bad behaviour and is in some sense “correct”. Whilst this is important it is also often useful to be able to reason not just about what will happen in the system, but also the dynamics of that behaviour: how long it will take, what are the probabilities of alternative outcomes, how much resource is used….? Such questions can be answered by quantitative analysis when information about timing and probability are incorporated into models of system behaviour.

In this short series of lectures I will talk about how we can extend formal methods to support quantitative evaluation as well as qualitative evaluation of systems. The first lecture will focus on computer systems and a basic approach based on the stochastic process algebra PEPA. In the second lecture I will introduce the language CARMA which is designed to support the analysis of collective adaptive systems, in which the structure of the system may change over time. In the third lecture I will consider systems where the exact details of behaviour may not be known and present the process algebra ProPPA which combines aspect of machine learning and inference with formal quantitative models.

Timetable:
Lecture 1: 9:30 – 10:30 – Performance Evaluation Process Algebra (PEPA)

Coffee break at 10:30 – 11:15
Lecture 2: 11:15 – 12:15 – Collective Adaptive Resource-sharing Markovian Agents (CARMA)

Lecture 3: 14:15 – 15:15 – Probabilistic Programming for Stochastic Dynamical Systems (ProPPA)

Venue: Upper and Lower College Halls