Director

Centre for Environmental Informatics, NIASRA
University of Wollongong, Australia

Distinguished Professor
School of Mathematics and Applied Statistics
University of Wollongong, Australia

Adjunct Professor
Department of Statistics, University of Missouri, USA

Affiliate
Jet Propulsion Laboratory (NASA), USA


Telephone 02 4221 5076
Email ncressie {at} uow.edu.au
Website https://niasra.uow.edu.au/cei/people/
UOW Scholars https://scholars.uow.edu.au/display/noel_cressie
ORCID https://orcid.org/0000-0002-0274-8050
LinkedIn Page https://www.linkedin.com/pub/noel-cressie/14/9a7/529
Curriculum Vitae Curriculum Vitae

Research Interests

Theory and applications of spatial and spatio-temporal stochastic models; Bayes and empirical-Bayes methods for hierarchical statistical models; environmental informatics; statistics for remote sensing.


Publications

BOOKS

2019

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

2011

Cressie, N. and Wikle, C.K. (2011). Statistics for Spatio-Temporal Data. Wiley, Hoboken, NJ (588 pp.).

1993

Cressie, N. (1993). Statistics for Spatial Data, rev.edn. Wiley, New York, NY (900 pp.). (Original edition, 1991. Paperback edition in the Wiley Classics Library: Wiley, Hoboken, NJ, 2015).

1988

Read, T.R.C. and Cressie, N. (1988). Goodness-of-fit Statistics for Discrete Multivariate Data. Springer, New York, NY (211pp.).

RESEARCH PUBLICATIONS AVAILABLE FOR DOWNLOAD

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.edu.au
Website https://andrewzm.wordpress.com
UOW Scholars https://scholars.uow.edu.au/display/andrew_zammit_mangion
ORCID https://orcid.org/0000-0002-4164-6866
GitHub https://github.com/andrewzm
Twitter https://twitter.com/andrewzm
LinkedIn https://uk.linkedin.com/in/andrewzammitmangion
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).


Books

2019

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

2013

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

2020

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.

2018

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 Analysis123, 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 Sensing10(1), 155.

2016

Cseke, B., Zammit-Mangion, A., Sanguinetti, G., and Heskes, T. (2016). Sparse approximations in spatio-temporal point-process models, Journal of the American Statistical Association111(516), pp. 1746–1763.

Cressie, N., and Zammit-Mangion, A. (2016). Multivariate spatial covariance models: A conditional approach, Biometrika103(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 Statistics18(A), pp. 194–220.

2015

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.

2012

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.

2011

Zammit Mangion, A., Yuan, K., Kadirkamanathan, V., and Sanguinetti, G. (2011). Online variational inference for state-space models with point-process observations. Neural Computation23(8), 1967-1999.

 CLICK HERE FOR FULL PUBLICATION LIST

Lecturer

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


Telephone 02 4298 1358
Email mmoores {at} uow.edu.au
Website https://niasra.uow.edu.au/cei/people/
UOW Scholars https://scholars.uow.edu.au/display/matt_moores
Wordpress http://mattstats.wordpress.com/
GitHub https://github.com/mooresm
Twitter https://twitter.com/MooresMt
LinkedIn https://www.linkedin.com/in/matthewmoores/
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).


Publications

2020

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 Analysis15(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.

2018

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. Theranostics8(22), 6195.

Drovandi, C.C., Moores, M.T., and Boys, R.J. (2018). Accelerating pseudo-marginal MCMC using Gaussian processes. Computational Statistics and Data Analysis118, 1-17.

2016

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 Chemistry88(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 Sciences63(1), 48-58.

2015

Moores, M.T., Drovandi, C.C, Mengersen, K., and Robert, C.P (2015). Pre-processing for approximate Bayesian computation in image analysis. Statistics and Computing25, 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 Analysis86, 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 Statistics22(3), 571-600.

2011

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. Traffic12(5), 627-643.

Honorary Research Fellow

School of Computer Science and Statistics, Trinity College Dublin, Ireland and 
Centre for Environmental Informatics, NIASRA
School of Mathematics and Applied Statistics
University of Wollongong, Australia


Telephone 02 4221 3725
Email ngja {at} tcd.ie
Website https://niasra.uow.edu.au/cei/people/
ORCID https://orcid.org/0000-0002-4405-4911
Google Scholar Page https://scholar.google.com/citations?user=a2ahoJIAAAAJ&hl=en

Research Interests

My research involves developing statistical models and inference techniques that reveal structure in data with complex dependency. This work includes developing mixture modelling and latent variable modelling to uncover clusters and describe dependencies in complex data, including relational/network data and data generated from counting processes. My research is motivated by the fact that complex data such as network data are increasingly prevalent in many areas of applications, including social sciences, finance, and biology. The emergence of such data sets requires developing effective and practical statistical tools.

In addition to statistical methodological research, I have research interests in the applications of statistics and machine learning to finance, health science and epidemiology.
 


Selected Publications

2020

Ng T.L.J., Murphy, T.B., Model-based Clustering of Count Processes, Journal of Classification, accepted.

Yang L., Ng T.L.J., Smyth B., Dong R. (2020), HTML: Hierarchical Transformer-based Multi-task Learning for Volatility PredictionProceedings of The Web Conference 2020, 441-451.

2019

Ng T.L.J., Murphy, T.B. (2019), Estimation of the intensity function of an inhomogeneous Poisson process with a change‐point, Canadian Journal of Statistics, 47 (4), 604-618.

Ng T.L.J., Murphy, T.B. (2019), Generalized Random Dot Product graph (2019), Statistics & Probability Letters, 148, 143 – 149.

Fosdick, B.K., McCormick, T.H., Murphy, T.B., Ng T.L.J., and Westling, T. (2019), Multiresolution network models. Journal of Computational and Graphical Statistics, 28 (1), 185-196.

Yang L., Xu Y., Ng T.L.J., Dong R., (2019), Leveraging BERT to Improve the FEARS Index for Stock ForecastingProceedings of the First Workshop on Financial Technology and Natural Language Processing (FinNLP@IJCAI 2019), 54-60.

2017

Yang L., Ng T.L.J., Mooney C., Dong R., (2017), Multi-level Attention-Based Neural Networks for Distant Supervised Relation ExtractionProceedings of Irish Conference on Artificial Intelligence and Cognitive Science, 206-218.

Lecturer

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


Telephone 02 4221 3825 
Email dgunawan{at}uow.edu.au
Website https://niasra.uow.edu.au/cei/people/
UOW Scholars https://scholars.uow.edu.au/display/david_gunawan
Personal Webpage https://sites.google.com/site/davidgunawan40/
Curriculum Vitae Curriculum Vitae

Research Interests

I have a general and broad interest in Bayesian computations, from both a methodological and an applied perspective. From a methodological perspective, I am interested in posterior simulation methods, such as Markov chain Monte Carlo, Sequential Monte Carlo, particle Markov chain Monte Carlo, Variational approximations, and Approximate Bayesian computation methods. From an applied perspective, I use Bayesian computational methods to solve real-world problems in many areas, such as economic inequality and poverty measurement, health, cognitive psychology, finance, economics, and environmental studies.


Publications 

REFEREED JOURNAL ARTICLES

2020

Gunawan, D., Khaled, M., and Kohn, R. (2020). Mixed marginal copula modeling. Journal of Business and Economic Statistics38(1), 137-147.

Gunawan, D., Hawkins, G., Tran, M. N., Kohn, R., and Brown, S. (2020). New estimation approaches for the hierarchical linear ballistic accumulator model. In press with Journal of Mathematical Psychology.

Lander, D., Gunawan, D., Griffiths, W. E., and Chotikapanich, D. (2020). Bayesian assessment of Lorenz and stochastic dominance. In press with Canadian Journal of Economics.

Gunawan, D., Panagiotelis, A., Griffiths, W. E., and Chotikapanich, D. (2020). Bayesian weighted inference from surveys. In press with Australian and New Zealand Journal of Statistics.

Mendes, E., Carter, C., Gunawan, D., and Kohn, R. (2020). A flexible particle Markov chain Monte Carlo method. In press with Statistics and Computing.

Chin, V., Gunawan, D., Fiebig, D. G., Kohn, R., and Sisson, S. (2020). Efficient data augmentation for multivariate probit models with panel data: an application to general practitioner decision making about contraceptives. In press with Journal of Royal Statistical Society Series C.

Tran, M. N., Scharth, M., Gunawan, D., Kohn, R., Brown, S., and Hawkins, G. E. (2020). Robustly estimating marginal likelihood for cognitive models via importance sampling. In press with Behavior Research Methods.

Wall, L., Gunawan, D., Brown, S., Tran, M. N., Kohn, R., and Hawkins, G. E. (2020). Identifying relationships between cognitive processes across tasks, contexts, and time. In press with Behavior Research Methods.

2019

Gunawan, D., Tran, M. N., Suzuki, K., Dick, J., and Kohn, R. (2019). Computationally efficient bayesian estimation of high dimensional archimedian copulas with discrete and mixed margins. Statistics and Computing29, 933-946.

2018

Gunawan, D., Griffiths, W. E., and Chotikapanich, D. (2018). Bayesian inference for health inequality and welfare using qualitative data. Economics Letters162, 76-80.

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Research Fellow

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


Telephone 02 4239 2388
Email michael_bertolacci {at} uow.edu.au
Website https://mbertolacci.github.io/
UOW Scholars https://scholars.uow.edu.au/display/michael_bertolacci

Research Interests

I am interested in large scale spatio-temporal problems in environmental statistics. My previous research involved developing hierarchical Bayesian mixture models for the analysis of Australian daily rainfall at the continental scale. I also investigated methods for modelling multiple nonstationary time series in the spectral domain, as applied to spatial datasets including monthly rainfall and measles epidemiology.

My current research focuses on spatio-temporal flux inversion for trace gases using remotely sensed data.


Publications

2019

Bertolacci, M., Cripps, E., Rosen, O., Lau, J. W., S. Cripps. (2019). Climate inference on daily rainfall across the Australian continent, 1876–2015. Annals of Applied Statistics13(2), pp 683–712. doi: 10.1214/18-AOAS1218.

2016

Bertolacci, M., Cripps, E., Cripps, S., Lau, J. W. (2016). Bayesian mixture models for multivariate time series with an application to Australian rainfall data. Neural Information Processing Systems Time Series Workshop. Presented a poster at this workshop.

2007

Wirth, A., Bertolacci, M. (2007). Are approximation algorithms for consensus clustering worthwhile? SIAM International Conference on Data Mining.

2006

Wirth, A., Bertolacci, M. (2006). New algorithms research for first year students11th Annual ACM Conference on Innovation and Technology in Computer Science Education (ITICSE ’06), pp 128–32.

Honorary Research Fellow

School of Statistics and Data Science, Nankai University, China and 
Centre for Environmental Informatics, NIASRA
School of Mathematics and Applied Statistics
University of Wollongong, Australia


Email bohaizhang {at} nankai.edu.cn
Website http://niasra.uow.edu.au/cei/people

Research Interests

Statistical methodology for modelling large spatial/spatio-temporal datasets; uncertainty quantification of large computer experiments; Gaussian process models; composite likelihood method; Bayesian hierarchical modelling; and Bayesian clustering/partitioning methods.

Past and current projects include: spatio-temporal modelling of Eastern United States ozone datasets, uncertainty quantification of a carbon capture unit, spatial modelling of yearly total precipitation anomalies in the United States, validation and bias correction of remote sensing data, and spatio-temporal dynamic modeling of Arctic sea-ice extent data.


Publications

2020

Zhang, B. and Cressie, N. (2020). Bayesian spatio-temporal modeling of Arctic sea ice extent. Bayesian Analysis, 15, 605-631.

2019

Zhang, B., Sang, H., and Huang, J. Z. (2019). Smoothed full-scale approximation of Gaussian process models for computation of large spatial datasets. Statistica Sinica29, 1711-1737.

Zhang, B., Cressie, N., and Wunch, D. (2019). Inference for errors-in-variables models in the presence of systematic errors with an application to a satellite remote sensing campaign. Technometrics61, 187-201.

Zhang, B. and Cressie, N. (2019). Estimating spatial changes over time of Arctic sea ice using hidden 2x2 tables. Journal of Time Series Analysis40, 288-311.

2017

Zhang, B., Cressie, N., and Wunch, D. (2017). Statistical properties of atmospheric greenhouse gas measurements looking down from space and looking up from the ground. Chemometrics and Intelligent Laboratory Systems162, 214-222.

Cressie, N., Burden, S., Shumack, C., Zammit-Mangion, A., and Zhang, B. (2017). Environmental Informatics. Wiley StatsRef : Statistics Reference Online, pp. 1-8 (doi:10.1002/9781118445112.stat07717.pub2).

2015

Zhang, B., Konomi, B. A., Sang, H., Karagiannis, G., and Lin, G (2015). Full scale multi-output Gaussian process emulator with nonseparable auto-covariance functions. Journal of Computational Physics300, 623-642.

Zhang, B., Sang, H., and Huang, J.Z. (2015). Full-scale approximations of spatio-temporal covariance models for large datasets. Statistica Sinica25, 99-114.

IT Technical Officier

National Institute for Applied Statistics Research Australia
School of Mathematics and Applied Statistics
University of Wollongong, Australia 


Telephone 02 4298 1498
Email ycao {at} uow.edu.au
Website https://niasra.uow.edu.au/cei/people/
LinkedIn Page https://www.linkedin.com/in/yicaoatau/

Responsibilities

I provide a range of IT and technical support services for the staff and students of NIASRA and I support research computing for SMAS. My role is multifaceted: I support a range of infrastructure, multiple operating systems, and the complex application needs of research computing. A fundamental part of my role is the provision of management, maintenance, and technical support for the high performance computing (HPC) infrastructure. In addition, I assist statistical researchers in scaling up from desktop computing to HPC or 'Big Data,' and I liaise with EIS-IT and IMTS to ensure the smooth operation of our systems within the broader UOW computing ecosystem.  I am also a team member of the CEI research team, contributing to the research projects such as flux inversion using OCO-2 satellite remote sensing data. My interests are networking, computer system/software design and development, and data processing and analysis.


Publications

2020

Cao, Y., and Veitch, D. (2020). Toward trusted time: remote server vetting and the misfiring heart of internet timing. IEEE/ACM Transactions on Networking. pp. 1-13. 10.1109/TNET.2020.2977024.

2019

Cao, Y., and Veitch D. (2019).  Where on earth are the best-50 time servers?. In Choffnes D., Barcellos M. (eds) Passive and Active Measurement. PAM 2019. Lecture Notes in Computer Science, vol. 11419. Springer

2018

Cao, Y., and Veitch, D. (2018). Network timing, weathering the 2016 leap second, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications, pp. 1826-1834.

Administrative Assistant

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


Telephone 02 4221 5076
Fax 02 4221 4998
Email karink {at} uow.edu.au
Website http://niasra.uow.edu.au/cei/people/

Responsibilities

I am responsible for key aspects of the day-to-day running of the National Institute for Applied Statistics Research Australia (NIASRA) and, in particular, of the Centre for Environmental Informatics. I perform a wide range of administrative functions to support the research carried out by NIASRA staff and students, and their collaborators. My responsibilities include making travel arrangements for members of NIASRA and their visitors; the administration of contracts, grants, and fellowships; and financial record-keeping. In addition, I liase with administrative departments from the wider UOW community, and I am a welcoming presence for visitors to NIASRA. I also manage aspects of our presence on the web, including the upkeep of the NIASRA Working Paper Series.