| Project title | RSFAS Researchers | Supporting organisations | Project commencement year |
|---|---|---|---|
| ARC Discovery Project: Responsible Statistical Learning: Uncertainty, Fairness and Transparency | Associate Professor Yanrong Yang | Australian Research Council | 2026 |
| ARC Discovery Project: Modern sufficient dimension reduction methods for complex dependent data | Associate Professor Francis Hui | Australian Research Council | 2026 |
| Gem-Cu: Geodynamic Environments for Mineralisation of Copper | Associate Professor Janice Scealy | Rio Tinto Centre for Future Materials | 2026 |
| ARC Discovery Project: Modern statistical methods for clustering community ecology data | Associate Professor Francis Hui | Australian Research Council | 2024 |
| Rainfall enhancement measurement: Post-doctoral research | Professor Alan Welsh, Dr Le Chang , Dr Xuan Liang, Dr Gen Nowak, Dr Anton Westveld | Hanbury Foundation | 2024 |
| Analytics for the Australian Grains Industry (AAGI) | Dr Emi Tanaka, Associate Professor Francis Hui, Professor Alan Welsh. | Grains Research Development Corporation | 2024 |
| ARC Discovery Project: Reliable and accurate statistical solutions for modern, complex data | Professor Alan Welsh, Associate Professor Francis Hui | Australian Research Council | 2023 |
| ARC Linkage Project: Surveillance and sampling to maintain absence of pests and diseases | Associate Professor Robert Clark | Australian Research Council | 2023 |
| ARC Discovery Project: Feature Learning for High-dimensional Functional Time Series | Associate Professor Yanrong Yang | Australian Research Council | 2023 |
| ARC Discovery Project: The impact of COVID-19 economic stimulus measures on corporate stakeholders | Professor Antje Berndt | Australian Research Council | 2022 |
| ARC Discovery Project: Novel statistical methods for data with non-Euclidean geometric structure | Professor Andrew Wood, Associate Professor Janice Scealy | Australian Research Council | 2022 |
| Healthy Minds, Healthy Bodies | Dr Jananie William | Commonwealth Department of Health | 2021 |
| Time Series Modeling and Prediction of Circular Data | Professor Alan Welsh | CSIRO and Data61 |
ARC Discovery Project: Responsible Statistical Learning: Uncertainty, Fairness and Transparency
This project seeks to create a new framework for statistical analysis that improves prediction accuracy, fairness, and transparency, while also accounting for uncertainty in data over time and space. It focuses on improving statistical methods for complex data, particularly in addressing the challenges of climate change’s impact on insurance pricing. The goal is to develop fairer, more reliable methods for pricing life insurance and planning for retirement, with a focus on ensuring better outcomes for all. The research aims to reduce inequalities and improve public health and social services, ultimately helping Australians adapt to climate change. Lead Chief Investigator for this ANU-led ARC Discovery project is Associate Professor Yanrong Yang (RSFAS). The project commenced in 2026.
ARC Discovery Project: Modern sufficient dimension reduction methods for complex dependent data
This project aims to develop a suite of modern statistical theory and methods for sufficient dimension reduction in data exhibiting complex dependence structures. In doing so, it will address a pressing need for statistical tools that can accurately distil high-dimensional regression and classification relationships, with little to no loss of information, into results readily understood by domain experts. The project is expected to unlock valuable insights into how various spatial, temporal, and sampling processes operate together to drive dynamics in bioinformatics and social network data. This will provide important long-term benefits to enhance biological discovery and combat the spread of misinformation in online digital environments. Associate Professor Francis Hui (RSFAS) is ANU Chief Investigator for this ARC Discovery Project led by the University of Sydney. The project commenced in 2026.
Gem-Cu: Geodynamic Environments for Mineralisation of Copper
This research project is part of the ANU Rio Tinto Centre for Future Materials. The project will use world-leading tools to map known copper deposits through space and time, integrating geological and geophysical datasets to identify the tectonic regimes and deep Earth dynamics triggering their formation. The project team will create a robust 4-D geodynamic framework that pinpoints the conditions necessary for copper ore genesis and their evolution. With the help of machine learning, the team will turn this deep-time science into predictive models. This project will make copper exploration more efficient, cost-effective, and sustainable, reducing reliance on intrusive and environmentally disruptive techniques. Associate Professor Janice Scealy (RSFAS) is a member of the research team led by Professor Rhodri Davies from ANU. The project also includes researchers from Imperial College London and University of British Columbia. The project commenced in 2026.
ARC Discovery Project: Modern statistical methods for clustering community ecology data
This project will develop statistical methods and software for clustering community ecology data, and use them to analyse systematic survey and citizen science program data collected along the Great Barrier Reef. By doing so, the project will address the dearth of statistical classification techniques for high-dimensional, multi-response data with complex relationships. Lead Chief Investigator for this ANU-led ARC Discovery project is Associate Professor Francis Hui (RSFAS). The project commenced in 2024.
Rainfall enhancement measurement: Post-doctoral research
This project, funded by the Handbury Foundation, aims to explore statistical methodology for the statistical analysis of rainfall enhancement data. Rainfall enhancement technologies try to increase the amount of rainfall reaching the ground when it rains. The project will be carried out with the support and co-operation of Professor Ray Chambers who has designed and analysed several studies. RSFAS researchers are Professor Alan Welsh, Dr Le Chang , Dr Xuan Liang, Dr Gen Nowak, and Dr Anton Westveld. The project commenced in 2024.
Analytics for the Australian Grains Industry (AAGI)
Analytics for the Australian Grains Industry (AAGI) is a strategic partnership aimed at harnessing analytics to drive the grain sector’s profitability and global competitiveness. ANU is partnering with Strategic Partners of the AAGI which is funded by the Grains Research Development Corporation. This collaborative partnership will focus on co-designed activities that will form a component of the AAGI Research and Development Program but also includes activities from the AAGI Service & Support and Upskilling & Awareness Programs allocated through AAGI. Lead Investigator for this project is Dr Emi Tanaka (RSFAS). Other RSFAS researchers are Associate Professor Francis Hui and Professor Alan Welsh. The project commenced in 2024.
ARC Discovery Project: Reliable and accurate statistical solutions for modern, complex data
This project aims to develop novel methods for reliable and accurate statistical modelling with modern, complex correlated and error-prone data. These methods will be invaluable in a range of contexts from protecting endangered marine species to better understanding the relationship between education achievement and financial success. ANU Chief Investigators for this ANU-led ARC Discovery project are Professor Alan Welsh and Associate Professor Francis Hui (RSFAS). The project commenced in 2023.
ARC Linkage Project: Surveillance and sampling to maintain absence of pests and diseases
This project aims to develop empirically validated statistical and mathematical methods for industry and government to deliver more efficient biosecurity surveillance programs. The project endeavours to enhance biosecurity at the border and within Australia, while minimising the costs and burden of testing. Chief Investigator Associate Professor Robert Clark and Postdoctoral Fellow Dr Sumon Das (RSFAS) are part of a cross-disciplinary team led by Kathryn Glass from ANU, with the Department of Agriculture, Fisheries and Forestry as linkage partner. The project commenced in 2023.
ARC Discovery Project: Feature Learning for High-dimensional Functional Time Series
This project aims to develop new methods and theories for common features on high-dimensional functional time series observed in empirical applications. Expected outcomes involve advances in big data theory and easy-to-implement algorithms for applied researchers. This project benefits not only advanced manufacturing by finding optimal stopping time for wood panel compression, but also superior forecasting for mortality in demography, climate data in environmental science, asset returns in finance, and electricity consumption in economics. Lead Chief investigator for this ANU-led ARC Discovery project is Associate Professor Yanrong Yang (RSFAS). The project commenced in 2023.
ARC Discovery Project: The impact of COVID-19 economic stimulus measures on corporate stakeholders
Australia's economic response to COVID-19 saw cash injections to companies and bailouts of some insolvent firms. This project aims to quantify the market value of these government subsidies and how it was shared across corporate stakeholders. Lead Investigator for this ANU-led project is Professor Antje Berndt (RSFAS). The project commenced in 2022.
ARC Discovery Project: Novel statistical methods for data with non-Euclidean geometric structure
This project aims to develop new flexible regression models and classification algorithms, along with robust and efficient inference methods, applicable to a wide range of non-Euclidean data types which arise in many fields of science, business and technology. The anticipated project outcomes will be of mathematical interest and valuable in applications such as finance; modelling electroencephalography data; Australian geochemical data; and Australian X-ray tumour image data. Chief Investigators for this ANU-led ARC Discovery project are Professor Andrew Wood (RSFAS) and Associate Professor Janice Scealy (RSFAS). The project commenced in 2022.
Healthy Minds, Healthy Bodies
The project, funded by the Commonwealth Department of Health, will provide advice on the use of actuarial techniques and perform data analysis on risks and life expectancy gaps for the Healthy Minds, Healthy Bodies Project. Lead researcher is Dr Jananie William (RSFAS). The project commenced in 2021.
Time Series Modeling and Prediction of Circular Data
This project, supported by CSIRO and Data61, will explore time series modeling and prediction of circular data. Wind direction data in particular has observations that naturally fall around a (360 degree) circle. While circular methods have been developed to characterise the centre and spread of data around the circle, these methods have only been extended into supervised learning methods such as regression in limited ways. This project aims to extend circular methods into the time-series domain, allowing better understanding and inference related to circular time series data. Further, these methods will be extended in such a way as to create synthetic circular time-series data that can recapture key features of such data in observed data sets, in particular for fine-scale wind direction data. Project researchers are Professor Alan Welsh (RSFAS) and Dr Carolyn Huston (Data 61).