Andrew Zammit Mangion

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

Telephone 02 4221 5112
Email azm {at}
UOW Scholars
Curriculum Vitae Curriculum Vitae

Research Interests

My research interests lie in spatio-temporal modelling and the tools that enable it. During my PhD at the University of Sheffield, (2008-2011), I focused on variational Bayesian methods for approximate inference of spatio-temporal log-Gaussian Cox process models. The methods I developed were successfully applied in conflict modelling. Following my PhD and a brief postdoc at the University of Edinburgh, I joined the University of Bristol (2012-2014). In my work there I 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 involved the use of the message-passing interface on a high-performance computer, parallel Gibbs sampling methods, and sparse linear algebra methods.

In my early years at NIASRA (2014-2017), my work focused on developing nonstationary, non-Gaussian, multivariate spatial models and software for spatial modelling.  In 2018, I took up a Discovery Early Career Research Award (DECRA) from the Australian Research Council (ARC), to investigate deep learning methods in spatio-temporal statistics, which is my current research focus.

I actively contribute software to the open-source community, and have written a number of reproducible packages intended solely to reproduce the results in published papers (see here and here for examples) as well  as some intended for use by the general scientific community (see here and here for examples).



Wikle, C.K., Zammit-Mangion, A., Cressie, N. (2019). Spatio-Temporal Statistics with R. Chapman & Hall/CRC, Boca Raton, FL.


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

Selected Publications


Zammit-Mangion, A., and Wikle, C.K. (2020). Deep integro-difference equation models for spatio-temporal forecasting, in press with Spatial Statistics.

Zammit-Mangion, A., and Cressie, N. (2020). FRK: An R package for spatial and spatio-temporal prediction with large datasets, in press with Journal of Statistical Software.


Zammit-Mangion, A., and Rougier, J.C. (2018). A sparse linear algebra algorithm for fast computation of prediction variances with Gaussian Markov random fields, Computational Statistics & Data Analysis, 123, pp. 116-130.

Zammit-Mangion, A., Cressie N., and Shumack, C. (2018). On statistical approaches to generate Level 3 products from statistical remote sensing retrievals, Remote Sensing, 10(1), 155.


Cseke, B., Zammit-Mangion, A., Sanguinetti, G., and Heskes, T. (2016). Sparse approximations in spatio-temporal point-process models, Journal of the American Statistical Association, 111(516), pp. 1746–1763.

Cressie, N., and Zammit-Mangion, A. (2016). Multivariate spatial covariance models: A conditional approach, Biometrika, 103(4), pp. 915–935.

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


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. Environmetrics26(3), 159-177.


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(31), 12414-12419. Awarded the Cozzarelli Prize from the National Academy of Sciences.


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(8), 1967-1999.


Last reviewed: 27 April, 2020