Andrew Zammit Mangion

Senior Lecturer
Centre for Environmental Informatics, NIASRA
School of Mathematics and Applied Statistics
University of Wollongong, Australia

Telephone 02 4221 5112
Fax 02 4221 4998
Email azm {at}
Curriculum Vitae Curriculum Vitae

Research Interests

My research interests lie in spatial and spatio-temporal modelling and the tools that enable it. In previous work, I have focused on variational Bayesian methods for approximate inference of spatio-temporal log-Gaussian Cox process models. These methods were successfully applied in conflict modelling and, in more recent research, I am working on approximate message passing algorithms in this context. In other related previous work, I have used well-established approximations to spatio-temporal multivariate processes to assess the Antarctic contribution to sea-level rise. For project details please see here. The project involved fusing multiple data products (from diverse satellites and research groups) through the use of a large-scale spatio-temporal model. Work here involved the use of the message-passing interface on a high-performance computer, parallel Gibbs sampling methods, sparse-matrix methodologies and has resulted in over five publications to date (for the latest publication see here).

My recent work at NIASRA has focused on multivariate spatial modelling and atmospheric trace-gas inversion. I have taken particular interest in this area since it forces me to move beyong the usual 'Gaussian' spatial models, it is inter-disciplinary, and requires an eye for careful computation. In a similar vein to my previous work on Antarctica, the work has extremely important implications, allowing one to quantify where the biggest flux sources and sinks are simply from observations of gas concentrations. Lately, I have also taken on reproducibility and software contribution to the open-source community more actively and have written a number of reproducible packages intended solely to reproduce the results in recent papers (see here and here) as well some intended for use by the general scientific community (see here and here). The latter one, focused on Fixed-Rank Kriging, is still under intense development.



Zammit Mangion, A., Dewar, M., Kadirkamanathan, V., Flesken, A., and Sanguinetti, G. (2013). Modeling Conflict Dynamics using Spatio-temporal Data. London, UK: Springer.



Martín-Español, A., Zammit Mangion, A., Clarke, P. J., Flament, T., Helm, V., King, M. A., Luthcke, S. B., Petrie, E., Remy, F., Schoen, N., Wouters, B., and Bamber, J. L. (2016). Spatial and temporal Antarctic ice sheet mass trends, glacio-isostatic adjustment and surface processes from a joint inversion of satellite altimeter, gravity and GPS data. Journal of Geophysical Research, 121, 182-200.

Cseke, B., Zammit-Mangion, A., Sanguinetti, G. and Heskes G. (2016). Sparse approximations in spatio-temporal point-process models. Journal of the American Statistical Association, 111, 1746–1763.

Martín-Español, A., King, M.A., Zammit-Mangion, A., Andrews, S.B., Moore, P., and Bamber, J.L. (2016). An assessment of forward and inverse GIA solutions for Antarctica. Journal of Geophysical Research: Solid Earth. 121, 6947–6965.

Cressie, N., and Zammit-Mangion, A. (2016) Multivariate spatial covariance models: A conditional approach. Biometrika, 103, 915–935.

Rougier, J.C., and Zammit-Mangion, A. (2016) Visualisation for large-scale Gaussian updates. Scandinavian Journal of Statistics, 43, 1153–1161.

Zammit-Mangion, A., Cressie, N., and Ganesan, A.L. (2016) Non-Gaussian bivariate modelling with application to atmospheric trace-gas inversion. Spatial Statistics, 18, 194–220.


Zammit Mangion, A., Cressie, N., Ganesan, A. L., O'Doherty, S., and Manning, A. J. (2015). Spatio-temporal bivariate statistical models for atmospheric trace-gas inversion. Chemometrics and Intelligent Laboratory Systems, 149, 227-241.

Schoen, N., Zammit Mangion, A., Rougier, J. C., Flament, T., Remy, F., Luthcke, S., and Bamber, J. (2015). Simultaneous solution for mass trends on the West Antarctic Ice Sheet. The Cryosphere, 9, 805-819.

Cseke, B., Zammit Mangion, A., Sanguinetti, G., and Heskes, T. (2015). Sparse approximations in spatio-temporal point-process models. Journal of the American Statistical Association, in press.

Zammit Mangion, A., Bamber, J., Schoen, N., and Rougier, J. (2015). A data-driven approach for assessing ice-sheet mass balance in space and time. Annals of Glaciology, 56, 175-183.

Zammit Mangion, A., Rougier, J., Schoen, N., Lindgren, F., and Bamber, J. (2015). Multivariate spatio-temporal modelling for assessing Antarctica's present-day contribution to sea-level rise. Environmetrics, 26, 159-177.

Cressie, N., Burden, S., Davis, W., Krivitsky, P. N., Mokhtarian, P., Suesse, T., and Zammit Mangion, A. (2015). Capturing multivariate spatial dependence: Model, estimate and then predict. Statistical Science, 30, 170-175.


Ganesan, A., Rigby, M., Zammit Mangion, A., Manning, A., Prinn, R., Fraser, P., Harth, C., Kim, K. R., Krummel, P. B., Li, S., Muhle, J., O'Doherty, S. J., Park, S.,Salameh, P. K., Steele, L., and Weiss, R. F. (2014). Characterization of uncertainties in atmospheric trace gas inversions using hierarchical Bayesian methods. Atmospheric Chemistry and Physics, 14, 3855-3864.

Zammit Mangion, A., Rougier, J., Bamber, J., and Schoen, N. (2014). Resolving the Antarctic contribution to sea-level rise: A hierarchial modelling framework. Environmetrics, 25, 245-264.


Menzies, R. I., Zammit Mangion, A., Hollis, L., Lennen, R., Jansen, M., Webb, D., Mullins, J. J., Dear, J. W., Sanguinetti, G., and Bailey, M. (2013). An anatomically unbiased approach for analysis of renal BOLD magnetic resonance images. American Journal of Physiology: Renal Physiology, 305, F845-F582.


Zammit Mangion, A., Dewar, M., Kadirkamanathan, V., and Sanguinetti, G. (2012). Point process modelling of the Afghan War Diary. Proceedings of the National Academy of Sciences (PNAS), 109, 12414-12419.

Zammit Mangion, A., Sanguinetti, G., and Kadirkamanathan, V. (2012). Variational estimation in spatiotemporal systems from continuous and point-process observations. IEEE Transactions on Signal Processing, 60, 3449-3459.


Zammit Mangion, A., Yuan, K., Kadirkamanathan, V., and Sanguinetti, G. (2011). Online variational inference for state-space models with point-process observations. Neural Computation, 23, 1967-1999.

Zammit Mangion, A., Sanguinetti, G., and Kadirkamanathan, V. (2011). A variational approach for the online dual estimation of spatiotemporal systems governed by the IDE. 18th IFAC World Congress, International Federation of Automatic Control, Milan, Italy, 3204-3209.

Zammit Mangion, A., Anderson, S. R., and Kadirkamanathan, V. (2011). Exploration and control of stochastic spatiotemporal systems with mobile agents. 18th IFAC World Congress, International Federation of Automatic Control, Milan, Italy, 4489-4494.


Mills, A. R., Apopei, B., Zammit Mangion, A., Barron-Gonzales, H., Gunetti, P., Thompson, H. A., and Garbett, P. (2010). Heterogeneous hardware technologies for accelerating complex aerospace system simulations. IEEE Areospace Conference United States, Institute of Electrical and Electronics Engineers, Big Sky, MT, USA.


Zammit Mangion, A. and Fabri, S. G. (2008). Experimental evaluation of haptic control for human activated command devices. UKACC Control 2008 Conference, Manchester, United Kingdom, 1-6.

Last reviewed: 4 February, 2017