Matt Moores

Matt Moores

Centre for Environmental Informatics, NIASRA

School of Mathematics and Applied Statistics
University of Wollongong, Australia

Telephone 02 4298 1358
Email mmoores {at}
UOW Scholars
Curriculum Vitae Curriculum Vitae

Research Interests

My research programme is focused on Bayesian inference and scalable computation for emerging applications in multi-spectral and hyper-spectral imaging. This complements NIASRA’s strength in statistical analysis of satellite remote sensing, while also expanding into exciting new areas of planetary exploration and biomedical imaging. The Mars 2020 rover will be equipped with the SuperCam instrument, which will use Raman, infra-red, and laser-induced breakdown spectroscopy (LIBS) to analyse rock and soil samples. These three modes of spectroscopy will provide complementary information, therefore my research aims to combine inference using Bayesian sensor fusion. As the spectral and spatial resolutions of instruments have improved, there has been an increasing need in analytical chemistry to process large volumes of complex data. Raman mapping in 2D and 3D enables nanometrology and imaging of biological processes at the molecular level. I am working on model-based approaches for source separation and quantification in this context. My sequential Monte Carlo (SMC) algorithm is available in the R package serrsBayes.

In previous work, I have developed accelerated algorithms for approximate Bayesian computation (ABC) and pseudo-marginal methods using surrogate models. A recent preprint is available here. I have implemented these algorithms in my R package bayesImageS, available on CRAN. I give a brief introduction to this research in this YouTube video, where I demonstrate image segmentation for satellite remote sensing and cone-beam computed tomography (CT).



Moores, M.T., Nicholls, G.K., Pettitt, A.N., and Mengersen, K. (2020). Scalable Bayesian inference for the inverse temperature of a hidden Potts model. Bayesian Analysis, 15(1), 1-27.

Moores, M.T., Pettitt, A.N., and Mengersen, K. (2020). Bayesian computation with intractable likelihoods. In Mengersen, K., Pudlo, P. & Robert, C.P. (eds.), Case Studies in Applied Bayesian Data Science, vol. 2259, Springer Nature, Cham, Switzerland.


Noonan, J., Asiala, S.M., Grassia, G., MacRitchie, N., Gracie, K., Carson, J., Moores, and M., et al. (2018). In vivo multiplex molecular imaging of vascular inflammation using surface-enhanced Raman spectroscopy. Theranostics, 8(22), 6195.

Drovandi, C.C., Moores, M.T., and Boys, R.J. (2018). Accelerating pseudo-marginal MCMC using Gaussian processes. Computational Statistics and Data Analysis, 118, 1-17.


Gracie, K., Moores, M., Smith, W. E., Harding, K., Girolami, M., Graham, D., and Faulds, K. (2016). Preferential attachment of specific fluorescent dyes and dye labelled DNA sequences in a SERS multiplex. Analytical Chemistry, 88(2), 1147-1153.

Hargrave, C., Mason, N., Guidi, R., Miller, J. A., Becker, J., Moores, M., Mengersen, K.,Poulsen, M., and Harden, F. (2016). Automated replication of cone beam CT‐guided treatments in the Pinnacle³ treatment planning system for adaptive radiotherapy. Journal of Medical Radiation Sciences, 63(1), 48-58.


Moores, M.T., Drovandi, C.C, Mengersen, K., and Robert, C.P (2015). Pre-processing for approximate Bayesian computation in image analysis. Statistics and Computing, 25, 22-33.

Moores, M.T., Hargrave, C.E., Deegan, T., Poulsen, M., Harden, F., and Mengersen, K. (2015). An external field prior for the hidden Potts model with application to cone-beam computed tomography. Computational Statistics and Data Analysis, 86, 27-41.

Falk, M. G., Alston, C. L., McGrory, C. A., Clifford, S., Heron, E. A., Leonte, D., Moores, M., Walsh, C. D., Pettitt, A.N., and Mengersen, K. (2015). Recent Bayesian approaches for spatial analysis of 2-D images with application to environmental modelling. Environmental and Ecological Statistics, 22(3), 571-600.


Beaumont, K. A., Hamilton, N. A., Moores, M., Brown, D. L., Ohbayashi, N., Cairncross, O., Cook, A.L., Smith, A.G., Misaki, R., Fukuda, M. and Taguchi, T. (2011). The recycling endosome protein Rab17 regulates melanocytic filopodia formation and melanosome trafficking. Traffic, 12(5), 627-643.


Last reviewed: 27 April, 2020