- Noel Cressie FRSN FAA
- Andrew Zammit Mangion
- Sumeetpal Sidhu Singh
- Matt Moores
- David Gunawan
- Michael Bertolacci
- Xiaotian Zheng
- Bohai Zhang
- James Ng
- 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.). |
Associate Professor
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 Research Profile | https://uow.discovery.symplectic.org/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. I have applied methods I developed to modelling conflict, to assessing the Antarctic contribution to sea-level rise, and to quantifying the sources and sinks of carbon dioxide on a global scale from satellite data. 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 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
|
2023 |
Ng, T.L.J., and Zammit-Mangion, A. (2023). Non-homogeneous Poisson process intensity modelling and estimation using measure transport, Bernoulli, 29(1), pp. 815-838. |
|
2022 |
Zammit-Mangion, A., Ng, T.L.J., Vu, Q., and Filippone, M. (2022). Deep compositional spatial models, Journal of the American Statistical Association, 117(540), pp. 1787-1808. |
|
2021 |
Zammit-Mangion, A., and Cressie, N. (2021). FRK: An R package for spatial and spatio-temporal prediction with large datasets, Journal of Statistical Software, 98(4), pp. 1-48. |
|
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. |
|
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), pp. 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), pp. 12414-12419. Awarded the Cozzarelli Prize from the National Academy of Sciences. |
TIBRA Foundation Chair of Mathematical Sciences and Professor
NIASRA and School of Mathematics and Applied Statistics
University of Wollongong, Australia
| Telephone | 02 4239 4508 |
| sumeetpals {at} uow.edu.au | |
| Website | https://niasra.uow.edu.au/cei/people/ |
| Google Scholars | https://scholar.google.com/citations?user=ljhwqoMAAAAJ&hl=en |
Background
I did my PhD at the University of Melbourne and then moved to the University of Cambridge. I was initially a postdoctoral research associate and then became a member of faculty in 2007. Before moving to UOW in January 2023, I was a Professor of Engineering Statistics and the Head of the Signal Processing and Communications Group in Department of Engineering at the University of Cambridge. I was also a Fellow and Director of Studies of Churchill College, and a Faculty Fellow of the Alan Turing Institute in the UK.
Research Interests
Bayesian statistics; Probabilistic Machine Learning; Computational statistics; Sequential Monte Carlo; Markov chain Monte Carlo; Reinforcement learning; Mathematical statistics; Time-series analysis.
Postgraduate positions
I am looking for motivated students interested in doing a PhD in the areas of computational statistics and its applications. If you have a strong mathematical background (especially in probability and statistics) and are interested in my areas of research (see above), feel free to email me.
Current PhD Students (Cambridge)
Jiaqi Guo (Oct. 2021--.)
Chon Wai Ho (Oct. 2021--.)
Ruiyang Jin (Oct. 2020--.)
Publications
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 computational methods for inverse problems and intractable likelihoods. I have developed accelerated algorithms for Bayesian synthetic likelihood (BSL), 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 my research in this YouTube video, where I demonstrate image segmentation for satellite remote sensing and cone-beam computed tomography (CT).
I am a Chief Investigator in the ARC Research Hub for Transforming Energy Infrastructure Through Digital Engineering (TIDE) and the Docent in Computational Statistics at Lappeenranta University of Technology (LUT), Finland. In previous work, I have developed model-based approaches for source separation and quantification of spectroscopy. My sequential Monte Carlo (SMC) algorithm is available in the R package serrsBayes. I give a brief demo of the R package in this YouTube video. I have applied these methods to surface-enhanced Raman spectroscopy (SERS) and coherent anti-Stokes Raman spectroscopy (CARS). In a recent preprint (available on arXiv), I have developed a method for peak detection using a point process model.
Selected publications
|
2022 |
Cressie, N.A. and Moores, M.T. (2022). Spatial Statistics. In Daya Sagar, B.S., Cheng, Q.M., McKinley, J., and Agterberg, F. (eds), Encyclopedia of Mathematical Geosciences, Springer Nature, Cham, Switzerland. |
|
2021 |
Berry, M.T., Nelson, M.I., Moores, M.T., Monaghan, B.J., and Longbottom, R.J. (2021). Using inert hot-spots to induce ignition within industrial stockpiles. In Proc. 15th Engineering Mathematics & Applications Conference, ANZIAM Journal, 63, C182-C194. |
|
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. |
Lecturer
Centre for Environmental Informatics, NIASRA
School of Mathematics and Applied Statistics,
University of Wollongong, Australia
| Telephone | 02 4239 4067 |
| 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.
Selected Publications
|
2022 |
Gunawan, D., Hawkins, G., Tran, M. N., Kohn, R., and Brown, S. (2022). Time-evolving psychological processes over repeated decisions. In press with Psychological Review. Nguyen, N., Tran, M. N., Gunawan, D., and Kohn, R. (2022). Statistical recurrent stochastic volatility model for stock markets. In press with Journal of Business and Economic Statistics. Dao, H., Gunawan, D., Tran, M. N., Kohn, R., Hawkins, G. E., and Brown, S. D. (2022). Efficient selection between hierarchical cognitive models: cross-validation with variational Bayes. In press with Psychological Methods. |
|
2021 |
Gunawan, D., Kohn, R., and Nott, D. (2021). Variational Bayes approximation of factor stochastic volatility models. International Journal of Forecasting, 37 (4), 1355-1375. |
|
2020 |
Gunawan, D., Khaled, M., and Kohn, R. (2020). Mixed marginal copula modeling. Journal of Business and Economic Statistics, 38(1), 137-147. 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., Dang, K. D., Quiroz, M., Kohn, R., and Tran, M. N. (2020). Subsampling sequential Monte Carlo for static Bayesian models. Statistics and Computing, 30 (6), 1741-1758. |
|
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. |
Senior 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/ |
| GitHub | https://github.com/mbertolacci/ |
| https://twitter.com/mikebertolacci | |
| 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 has involved developing hierarchical Bayesian mixture models for the analysis of Australian daily rainfall at the continental scale, investigating methods for modelling multiple nonstationary time series in the spectral domain, and developing methods for spatio-temporal flux inversion for trace gases. My current research focuses on spatio-temporal problems in offshore engineering.
Selected Publications
|
2023 |
Cressie, N., Zammit-Mangion, A., Jacobson, J., Bertolacci, M. (2023). Earth’s CO2 battle: a view from space, Significance, 20(1), pp 14–19. doi:10.1093/jrssig/qmad003. |
|
2022 |
Stell, A. C., Bertolacci, M., Zammit-Mangion, A., Rigby, M., Fraser, P. J., Harth, C. M., Krummel, P. B., Lan, X., Manizza, M., Mühle, J., O'Doherty, S., Prinn, R. G., Weiss, R. F., Young, D., and Ganesan, A. L. (2022). Understanding the growth of atmospheric nitrous oxide using a global hierarchical inversion, Atmospheric Chemistry and Physics, 22, 12945–12960. doi:10.5194/acp-22-12945-2022. Cressie, N., Bertolacci, M., Zammit-Mangion, A. (2022). From many to one: Consensus inference in a MIP, Geophysical Research Letters, 49, e2022GL098277. doi:10.1029/2022GL098277. Zammit-Mangion, A., Bertolacci, M., Fisher J., Stavert, A., Rigby, M., Cao, Y., Cressie, N. (2022). WOMBAT: A fully Bayesian global flux-inversion framework, Geophysical Model Development, 15, pp 45-73. doi:10.5194/gmd-15-45-2022. Bertolacci, M., Rosen, O., Cripps, E, S. Cripps. (2022). AdaptSPEC-X: Covariate-Dependent Spectral Modeling of Multiple Nonstationary Time Series. Journal of Computational and Graphical Statistics, 31(2), pp 436–454. doi:10.1080/10618600.2021.2000870. |
|
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. |
|
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 ecology, environmental science, health, and economics. 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 developing new statistical models for better understanding
of the Antarctic environment, as well as data fusion and statistical downscaling for regional climate variables with uncertainty quantification. For example, we study Antarctica’s biodiversity in response to changes in climate processes, using the fused and/or downscaled climate variables. 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 framework for direct spatial and spatiao-temporal modelling of non-Gaussian data. The framework is built around directed acyclic graphs that factorize the probability of a random vector into a product of conditional probabilities. We model these conditional probabilities using a structured mixture of local transition kernels. This provides a general strategy for direct, probabilitic modeling of general non-Gaussian data with efficient computation. If you are interested in the area and would like to discuss collaboration opportunities, please do not hesitate to contact me.
Selected Publications
|
2023 |
Zheng, X., Kottas, A., and Sansó, B. (2023). Bayesian geostatistical modeling for discretevalued processes. DOI:https://doi.org/10.48550/arXiv.2111.01840. Zheng, X., Kottas, A., and Sansó, B. (2023). Nearest-neighbor mixture models for non-Gaussian spatial processes. DOI:https://doi.org/10.48550/arXiv.2107.07736. |
|
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, 283-293. |
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
Bayesian hierarchical modelling; Statistical methodology for modelling large spatial/spatio-temporal datasets; spatial errors-in-variables model; spatial functional data analysis; point process; and environmental statistics.
Past and current projects include: spatio-temporal modelling of Eastern United States, spatio-temporal dynamic modelling of Arctic sea-ice-extent data, and Bayesian clustering of people’s mobility behaviors in Houston.
Selected Publications
|
2023 |
Cao, J., He, S., and Zhang, B. (2023). Spatial linear regression with covariate measurement errors: Inference and scalable computation in a functional modeling approach. Journal of Computational and Graphical Statistics, to appear. Zhang, B., Sang, H., Luo, Z.T., and Huang, H. (2023). Bayesian clustering of spatial functional data with an application to the clustering of mobility behaviors in Houston during the COVID-19 pandemic, Annals of Applied Statistics, 17, 583-605. |
|
2022 |
Zhang, B., Li, F., Sang, H., and Cressie, N. (2022). Inferring changes of Arctic sea ice through a spatio-temporal logistic autoregression fitted to remote-sensing data, Remote Sensing, 14, 5995. |
|
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). |
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
I am an Assistant Professor in Statistics and Data Science at Trinity College Dublin. Previously I was a Lecturer in Statistics at University of Wollongong. 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
| 2023 |
Ng T.L.J. & Zammit-Mangion A. (2023). Non-homogeneous Poisson process intensity modeling and estimation using measure transport, Bernoulli, 29(1): 815-838. Ng T.L.J. (2023). Penalized maximum likelihood estimator for mixture of von Mises–Fisher distributions, Metrika, 86, 181–203. |
| 2022 |
Ng T.L.J. & Zammit-Mangion A. (2022). Spherical Poisson point process intensity function modelling and estimation with measure transport, Spatial Statistics, 50, 100629. Zammit-Mangion A., Ng T.L.J., Vu Q., & Filippone M. (2022). Deep compositional spatial models, Journal of American Statistical Association, 117 (540), 1787-1808. |
| 2021 |
Ng T.L.J. & Murphy T.B. (2021). Model-based clustering of count processes, Journal of Classification, 38, 188-211. Ng T.L.J. & Murphy T.B., Westling T., McCormick T.H., & Fosdick B. (2021). Modeling the social media relationships of Irish politicians using a generalized latent space stochastic blockmodel, The Annals of Applied Statistics (2021), 15(4), 1923-1944. |
|
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. 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. |
Honorary Research Fellow
Faculty of Science, The University of Sydney
NIASRA, School of Mathematics and Applied Statistics
University of Wollongong, Australia
| ycao {at} uow.edu.au | |
| Website | https://niasra.uow.edu.au/cei/people/ |
| LinkedIn Page | https://www.linkedin.com/in/yicaoatau/ |
Responsibilities
I am working as senior Linux administrator in the Faculty of Science at The University of Sydney. I provide a range of IT and technical support services for Faculty staff and HDRs. 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.
At UOW, I am collabarating with statistical researchers to scale up from desktop computing to computing with 'Big Data.' I am on impactful research projects that make extensive use of NIASRA’s cutting-edge HPC cluster. Projects include carbon-dioxide flux inversion using satellite remote sensing data, spatio-temporal mapping of benthic sediments, and climate-model downscaling in Antarctica.
My interests are machine learning, networking, computer system/software design and development, and data processing and analysis.
Selected Publications
|
2022 |
Zammit-Mangion, A., Bertolacci, M., Fisher, J., Stavert, A., Rigby, M., Cao, Y., and Cressie, N. (2022). WOMBAT v1.0: a fully Bayesian global flux-inversion framework. Geoscientific Model Development, 15, 45-73. doi:10.5194/gmd-15-45-2022. Veitch D., Mani K., Cao Y. and Barford P. (2022). iHorology: Lowering the barrier to microsecond-level internet time. IEEE/ACM Transactions on Networking. doi:10.1109/TNET.2022.3174189. |
|
2021 |
Vu, Q., Cao, Y., Jacobson, J. et al. (2021) Discussion on “Competition on Spatial Statistics for Large Datasets”. Journal of Agricultural, Biological and Environmental Statistics, 26, 614–618. doi:10.1007/s13253-021-00464-0 |
|
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. |
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.