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Combining Formal Methods and Machine Learning

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Quantitative formal methods, such as stochastic process algebras, have been successfully applied in a number of application domains over the last 20 years. They offer rigorous techniques for asking questions about the dynamic behaviour of systems. In the last decade more data-driven approaches to system analysis, based on machine learning have gained prominence. Yet the two approaches have complementary strengths and weaknesses and should not necessarily be thought of as competing. In this talk I will talk about two pieces of work in which we have sought to combine machine learning techniques into a formal modelling framework.

Bio: 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 the Head of School.

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. She has developed high-level modelling languages for application domains ranging from computer systems, biological process and collective adaptive 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 is a member of Academia Europaea and a Fellow of the Royal Society of Edinburgh. She has published over 100 journal and conference papers and held several Research Council and European Commission grants.

Programme of the day:

  • 3.15 pm Wheeler Lecture in Lecture Theatre 1
  • 5.00 pm Drinks Reception

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