- Noel Cressie FRSN FAA
- Andrew Zammit Mangion
- Matt Moores
- James Ng
- David Gunawan
- Michael Bertolacci
- Xiaotian Zheng
- Bohai Zhang
- Yi Cao
- Karin Karr
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 |
ncressie {at} uow.edu.au | |
Website | https://niasra.uow.edu.au/cei/people/ |
Curriculum Vitae | Curriculum Vitae |
https://twitter.com/NoelCressie?s=20 | |
UOW Scholars | https://scholars.uow.edu.au/display/noel_cressie |
Wikipedia | https://en.wikipedia.org/wiki/Noel_Cressie |
ORCID | https://orcid.org/0000-0002-0274-8050 |
LinkedIn Page | https://www.linkedin.com/pub/noel-cressie/14/9a7/529 |
Research Interests
Data science; spatio-temporal statistics; hierarchical Bayesian modelling and computation; environmental informatics; remote sensing.
Books
2019 |
Wikle, C.K., Zammit-Mangion, A., and Cressie, N. (2019). Spatio-Temporal Statistics with R. Chapman & Hall/CRC, Boca Raton, FL (380 pp.). |
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 (211 pp.). |
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 |
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 |
https://twitter.com/andrewzm | |
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. 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 bicon and atminv for examples) as well as some intended for use by the general scientific community (see EFDR and FRK 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 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. |
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 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. |
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. Environmetrics, 26(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 Computation, 23(8), 1967-1999. |
Lecturer
Centre for Environmental Informatics, NIASRA
School of Mathematics and Applied Statistics
University of Wollongong, Australia
Telephone | 02 4298 1358 |
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 |
https://twitter.com/MooresMt | |
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 at arXiv. 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 |
Härkönen, T., Roininen, L., Moores, M.T., and Vartiainen, E.M. (2020) Bayesian quantification for coherent anti-Stokes Raman scattering spectroscopy. Journal of Physical Chemistry B, 124(32), 7005-7012. 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. |
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. 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. |
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 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. |
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 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. |
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 |
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 Prediction, Proceedings 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 Forecasting, Proceedings 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 Extraction, Proceedings 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 |
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 Statistics, 38(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 Computing, 29, 933-946. |
2018 |
Gunawan, D., Griffiths, W. E., and Chotikapanich, D. (2018). Bayesian inference for health inequality and welfare using qualitative data. Economics Letters, 162, 76-80. |
Research Fellow
Centre for Environmental Informatics, NIASRA
School of Mathematics and Applied Statistics
University of Wollongong, Australia
Telephone | 02 4239 2388 |
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 Statistics, 13(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 students, 11th Annual ACM Conference on Innovation and Technology in Computer Science Education (ITICSE ’06), pp 128–32. |
Research Fellow
Centre for Environmental Informatics, NIASRA
School of Mathematics and Applied Statistics
University of Wollongong, Australia
xzheng {at} uow.edu.au | |
Website | https://www.xzheng42.com/ |
Research Interests
I have a broad interest in parametric and nonparametric methods for complex and dependent data, from a Bayesian perspective. I draw motivation from applications in epidemiology, biology, environmental science, and the social sciences. I am also interested in methods for high-dimensional data and scalable algorithms for large data sets.
My current research at the Centre for Environmental Informatics involves data fusion and statistical downscaling for regional climate variables with uncertainty quantification. These variables will be used to understand the climate processes and their relationship to the ecology in Antarctica. The research is part of Securing Antarctica's Environmental Future (SAEF), a Special Research Initiative funded by the Australian Research Council.
Another line of my current research is focused on the development of a modeling framework for non-Gaussian dependent data, including time series, spatial data, and point patterns. The framework is built around directed graphical models with mixtures. This idea of conditional modelling provides the possibility of modelling non-Gaussian dependent data in a unified manner. The research is ongoing and has various interesting directions to explore. If you are interested in the area and would like to discuss collaboration opportunities, feel free to e-mail me.
Publications
2022 |
Zheng, X., Kottas, A., & Sansó, B. (2022). On construction and estimation of stationary mixture transition distribution models. Journal of Computational and Graphical Statistics, 31(1), 283-293. |
2021 |
Zheng, X., Kottas, A., & Sansó, B. (2021). Bayesian geostatistical modeling for discrete-valued processes. arXiv preprint arXiv:2111.01840. |
2021 |
Zheng, X., Kottas, A., & Sansó, B. (2021). Nearest-neighbor mixture models for non-gaussian spatial processes. arXiv preprint arXiv:2107.07736. |
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
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 Sinica, 29, 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. Technometrics, 61, 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 Analysis, 40, 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 Systems, 162, 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 Physics, 300, 623-642. Zhang, B., Sang, H., and Huang, J.Z. (2015). Full-scale approximations of spatio-temporal covariance models for large datasets. Statistica Sinica, 25, 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 |
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 |
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.