Research

My work investigates Bayesian statistical and computational methods for inference in large-scale dynamical systems. I am currently working in environmental informatics, developing methods to meet the challenges of spatially resolved biogeochemical models. Previously I worked on sequential Monte Carlo (particle filtering) techniques for continuous-time dynamical systems modelled using stochastic differential equations, applied to fMRI. A large part of my current work involves general purpose GPU programming and high performance computing.

I am currently a computational scientist at CMIS, CSIRO in Perth, Australia. Previously I was a PhD student in machine learning at the School of Informatics, University of Edinburgh.

Publications

• Murray, L.M. (2009) Bayesian Learning of Continuous Time Dynamical Systems (with applications in Functional Magnetic Resonance Imaging). PhD thesis. [full text]
• Murray, L.M. and Storkey, A.J. (2008) Continuous Time Particle Filtering for fMRI. Advances in Neural Information Processing Systems, 20, 1049-1068. [full text] [poster]
• Storkey, A.J., Simonotto, E., Whalley, H., Lawrie, S., Murray, L.M. and McGonigle, D. (2007) Learning structural equation models for fMRI. Advances in Neural Information Processing Systems, 19, 1329-1336.

Software

The dysii Dynamic Systems Library is a direct product of my research work to date.

I’ve posted the final version of my PhD thesis, "Bayesian Learning of Continuous-Time Dynamical Systems, with Applications in Functional Magnetic Resonance Imaging" to the research page. Now assessed, corrected and passed!
Note that this may serve as a useful manual for some of the detail behind the algorithms of the dysii Dynamic Systems Library.

Version 1.4.0 of the dysii Dynamic Systems Library has been released. This is a major new release with a number of additional features and performance enhancements, as well as representing a consolidation of code and maturation of much of the API.
Particular new features include:

The kernel forward-backward and two-filter smoothers, suitable for fast, large-scale approximate inference [...]

I’ve made my PhD thesis available on the research page, "Bayesian Learning of Continuous Time Dynamical Systems (with Applications in Functional Magnetic Resonance Imaging)". The thesis considers Bayesian filtering and smoothing for state and parameter estimation in general non-linear, non-Gaussian systems using stochastic differential models. It is the theoretical basis for impending updates to the [...]