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Embedding machine learning in formal stochastic models of biological processes

Jane Hillston ( University of Edinburgh )


Formal modelling languages such as process algebras are effective tools in computational biological modelling.  However, handling data and uncertainty in these representations in a statistically meaningful way is an open problem, severely hampering the usefulness of these elegant tools in many real biological applications. I will present ProPPA, a process algebra which incorporates uncertainty in the model description, supporting the use of Machine Learning techniques to integrate observational data in the modelling.  I will explain how this is given a semantics in terms of a generalisation of Constraint Markov Chains, and demonstrate how this can be used to perform inference over biological models.



Jane Hillston gained a BA in Mathematics from the University of York, UK in 1985 and an MS in Mathematics from Lehigh University, USA in 1987.  After a short spell in industry, she studied for a PhD in Computer Science at the University of Edinburgh, which was awarded in 1994.

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 was Director of the Laboratory for Foundations of Computer Science 2011–2014, and she is currently Director of Research and Deputy Head of the School of Informatics.

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.  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 serves on the editorial board of several journals including Theoretical Computer Science, Logical Methods in Computer Science and Transactions on Modelling and Computer Simulation.



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