Current grants

  • 2024 – 2025; King Abdullah University of Science and Technology (KAUST) Opportunity Fund Program; Neural estimators for fast optimal inference with intractable statistical models in complex settings; Andrew Zammit-Mangion (grant led by Raphaël Huser, KAUST)
  • 2023 – 2025; United States Asian Office of Aerospace Research and Development (AOARD); Bayesian spatio-temporal analysis and statistical computation in very high dimensional problems; Noel Cressie, Sumeetpal Singh, and Andrew Zammit-Mangion
  • 2023; UOW Learning & Teaching Innovation Grant; Reflection on work-integrated learning: A cross-disciplinary project advancing WIL; Laura Rook, Bonnie Dean, Michelle Eady, Grant Ellmers, Ashley Heath, Meredith Kennedy, Matthew Moores, Suzi Russell, Matalena Tofa, Erin Twyford, and Jordan Shepherd
  • 2021 – 2028; ARC Special Research Initiative (SRI); Securing Antarctica's environmental future (SAEF); Noel Cressie, Andrew Zammit-Mangion, and Xiaotian Zheng
  • 2021 – 2025; ARC Industrial Transformation Research Hub (ITRH); Transforming energy infrastructure through digital engineering (TIDE); Andrew Zammit-Mangion, Matthew Moores, David Gunawan, and Michael Bertolacci (grant led by Phillip Watson, University of Western Australia)
  • 2021 – 2024; ARC Discovery Project (DP); Bayesian inference for psychological theories with intractable likelihoods; David Gunawan (grant led by Robert Kohn, UNSW)
  • 2021 – 2023; UOW Research Committee Major Equipment Grant (MEG); Upgrade of the High-Performance Computer (HPC) at the National Institute for Applied Statistics Research Australia; Andrew Zammit-Mangion, Noel Cressie, David Gunawan, Matthew Moores, Pauline O'Shaughnessy, Ramethaa Pirathiban, and Thomas Suesse
  • 2019 – 2025; ARC Discovery Project (DP); Bayesian inversion and computation applied to atmospheric flux fields; Noel Cressie, Andrew Zammit-Mangion, and Michael Bertolacci
  • 2018 – 2024; ARC Discovery Early Career Research Award (DECRA); Deep space-time models for modelling complex environmental phenomena; Andrew Zammit-Mangion

Research projects

In the pages below, we provide a brief and somewhat informal description of our current research in environmental informatics. All research topics are linked to pages with more details and references. 



Statistical remote sensing

Remote sensing of geophysical variables almost always involves indirect measurements of energies in relevant bands of the electro-magnetic spectrum, taken by an instrument on board a satellite or some other flight vehicle. For any one measurement at a given location and time, inference on states of the atmosphere is carried out in the presence of uncertainty. 

Flux inversion of atmospheric trace gases

Flux inversion is the process through which we locate and quantify the sources and sinks of a gas using measurements of that gas at different points (or along different paths) in space and time. All flux inversion procedures need to take into account meteorology that can be used to predict how gas particles move in the atmosphere when released from a source. At CEI we are working on flux inversion at three scales, which we classify as local, regional, and global.

Local flux inversion

Local flux inversion is concerned with locating and quantifying point sources and sinks in a small area of interest, generally on the order of a few thousand square metres or a few square kilometres. At these scales, simple atmospheric models can be used, and precise quantification of emissions can be done. (Image credit: Stephan Muller)

Regional flux inversion

Regional flux inversion is concerned with locating and quantifying sources and sinks at much larger scales (spanning whole countries, or a continent). This inversion setup is useful for validating a country's emission portfolio, for example, to ensure that it is complying with any emission targets in place. (Image credit: NASA)

Global flux inversion

Global flux inversion is concerned with locating and quantifying sources and sinks at the global scale. Inversion at this scale requires the use of global atmospheric transport models, as well as sophisticated computational machinery to deal with massive data sets. (Image credit: NASA) 

Statistical learning for ocean engineering

Ocean engineering is a branch of engineering that is concerned with the design, construction, and maintenance of structures and systems in the ocean environment, including ships, offshore platforms, wind turbines, and underwater vehicles. Researchers from CEI have partnered with the University of Western Australia to develop data science methods that facilitate some of the processes, to help make them more environmentally friendly, safe, and viable. Below we discuss two sub-fields: one related to sediment analysis, and the other to wave prediction.

Sediment analysis

The analysis of sediments is a crucial component in many site investigations, before implementing offshore platforms or structures. We have developed GeoWarp, a 3D spatial data analysis tool designed for predicting sediment properties and associated uncertainties from sparse geotechnical data. (Image credit: Rampion Offshore Wind Farm, United Kingdom - picture by Nicholas Doherty)

Wave prediction

Being able to predict large waves in real-time is important for ensuring the safety and efficiency of several maritime activities. We have developed neural networks for time series forecasting that are able to predict large waves, and provide associated uncertainties, in advance of them reaching a floating platform or vessel. (Image credit: Timelapse photography of body of water - picture by Thierry Meier)

Space-based research in spectroscopy

Understanding the chemical composition of mineral and biological samples is vital for many practical applications, including the search for evidence of past life on Mars, environmental monitoring of coral health and water quality, and medical diagnostics of cancer and other diseases. (Image credit: NASA)

Multivariate stochastic modelling

Multivariate geostatistics is based on modelling all covariances between all possible combinations of two or more variables at any locations in a continuously indexed domain. Multivariate spatial covariance models need to be built with care, since any covariance matrix that is derived from such a model has to be nonnegative-definite. In our research we are trying to find new ways to easily construct valid complex multivariate covariance models. Two such ways that we present below are through conditioning, and through domain warping.

Conditional models

When using conditioning for model construction one only needs univariate covariance functions to yield valid multivariate models. The approach we develop has a vast array of applications since it features conditional dependencies on a potential causative process. The project on regional methane flux mapping given on the page, Regional flux inversion, is one such application.

Models on warped spaces

Complex multivariate spatial covariance models can be constructed by taking a very simple, valid, multivariate spatial covariance model, and then warping the spatial domain on which it is defined. We find that warping can easily induce properties such as nonstationarity and asymmetry.

Murujuga rock art monitoring program

The Murujuga Rock Art Monitoring Program (MRAMP) is overseen by the Murujuga Aboriginal Corporation (MAC) and Western Australia's Department of Water and Environmental Regulation (DWER). The purpose of MRAMP is to monitor, evaluate, and report on the changes and trends in the integrity or condition of the aboriginal rock art and its environment on the Murujuga (aka Burrup) Peninsular, near Karratha in Western Australia. This multidisciplinary research project involves University of Wollongong’s CEI Director Noel Cressie, who is a member of MRAMP’s statistical design and analysis team that provides advice to scientists (air quality, geology, organic chemistry, microbiology) at Curtin University. MRAMP is designed to determine whether anthropogenic emissions from nearby industry are changing the integrity or condition of the rock art and whether it is being subjected to accelerated change.