Unraveling ocean mixing and air-sea forcing along the Indo-Pacific exchange. This project aims to collect unprecedented observations and develop high resolution model simulations to examine changes in the Indonesian Throughflow (ITF) north of Australia. This project expects to develop new knowledge of ocean-atmosphere interactions along the path of the ITF from the Pacific to the Indian Ocean, which are the powerhouse that drives changes in winds and rainfall around Australia and the entire Indo ....Unraveling ocean mixing and air-sea forcing along the Indo-Pacific exchange. This project aims to collect unprecedented observations and develop high resolution model simulations to examine changes in the Indonesian Throughflow (ITF) north of Australia. This project expects to develop new knowledge of ocean-atmosphere interactions along the path of the ITF from the Pacific to the Indian Ocean, which are the powerhouse that drives changes in winds and rainfall around Australia and the entire Indo-Pacific region. Expected outcomes include a 1000-fold increase in the observations of mixing in the Indonesian seas and new understanding of the ocean-atmosphere processes that control water property change along the ITF. This should lead to strong improvement in the skill of climate forecast models in the Australian region.Read moreRead less
Uncertainty, Risk and Related Concepts in Machine Learning. Machine learning is the science of making sense of data. It does not and cannot remove all risk and uncertainty. This project proposes to study the foundations of how machine learning uses, represents and communicates risk and uncertainty. It aims to do so by finding new theoretical connections between diverse notions that have arisen in allied disciplines. These include risk, uncertainty, scoring rules and loss functions, divergences, ....Uncertainty, Risk and Related Concepts in Machine Learning. Machine learning is the science of making sense of data. It does not and cannot remove all risk and uncertainty. This project proposes to study the foundations of how machine learning uses, represents and communicates risk and uncertainty. It aims to do so by finding new theoretical connections between diverse notions that have arisen in allied disciplines. These include risk, uncertainty, scoring rules and loss functions, divergences, statistics and different ways of aggregating information. By building a more complete theoretical map it is expected that new machine learning methods will be developed, but more importantly that machine learning will be able to be better integrated into larger socio-technical systems.Read moreRead less
Macroeconomic and Financial Modelling in an Era of Extremes. This project aims to develop methods to allow workhorse models in economics and finance to better reflect tail events--low probability extreme events, such as the Global Financial Crisis and the COVID-19 pandemic. It intends to address fundamental technical challenges in the estimation of such models, develop a coherent framework for counterfactual analysis of these models and propose methods to apply these models in a big-data environ ....Macroeconomic and Financial Modelling in an Era of Extremes. This project aims to develop methods to allow workhorse models in economics and finance to better reflect tail events--low probability extreme events, such as the Global Financial Crisis and the COVID-19 pandemic. It intends to address fundamental technical challenges in the estimation of such models, develop a coherent framework for counterfactual analysis of these models and propose methods to apply these models in a big-data environment. Expected outcomes include new insights into the transmission of tail risks in the global economic and financial system. This should provide significant benefits, including guidance to Australian and international policymakers charged with maintaining stability in the face of extreme events.Read moreRead less
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. The significance includes addressing a key gap in adaptive and efficient feature learning, improving forecasting accuracy and understanding forecasting-driven factors comprehensively for empirical data. Expected outcomes involve advances in big data theory and easy-to-implement algori ....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. The significance includes addressing a key gap in adaptive and efficient feature learning, improving forecasting accuracy and understanding forecasting-driven factors comprehensively for empirical data. 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. Read moreRead less
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. The project expects to make significant strides towards future-proofing statistical data analysis, equipping practitioners with a suite of robust and computationally efficient methods which provide confidence in the stability and reproducibility of results obtained, while offering guar ....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. The project expects to make significant strides towards future-proofing statistical data analysis, equipping practitioners with a suite of robust and computationally efficient methods which provide confidence in the stability and reproducibility of results obtained, while offering guarantees on their transferability over a range of populations. This will provide important benefits as they are applied in predicting endangered marine species for fisheries conservation, and in enhancing our national understanding of the relationship between education achievement and financial success. Read moreRead less
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. There are serious flaws with currently available methods of analysis for non-Euclidean data. This project expects to transform such analyses by providing new quanti ....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. There are serious flaws with currently available methods of analysis for non-Euclidean data. This project expects to transform such analyses by providing new quantitative tools within a unifying framework. The anticipated project outcomes will be of mathematical interest and valuable in applications such as finance (predicting Australian stock returns); modelling electroencephalography data; Australian geochemical data, relating to sediments; and Australian X-ray tumour image data. Read moreRead less
Nowcasting and Interpreting the Australian Economy. This project aims to investigate methods for nowcasting and interpreting the Australian economy. This is determining the current state of the economy and the factors contributing to it.
This project expects to generate new knowledge on how unconventional, new, data sources and innovative methods can be used to in nowcasting and how the Australian economy can be modelled.
The expected outcomes include timely new indicators of the state of the ec ....Nowcasting and Interpreting the Australian Economy. This project aims to investigate methods for nowcasting and interpreting the Australian economy. This is determining the current state of the economy and the factors contributing to it.
This project expects to generate new knowledge on how unconventional, new, data sources and innovative methods can be used to in nowcasting and how the Australian economy can be modelled.
The expected outcomes include timely new indicators of the state of the economy, and the factors contributing to it. This should provide significant benefits through informing the conduct of Australian macroeconomic policy, as the appropriate policy response depends not only on knowing the current state of the economy but understanding the economic factors underlying it.
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New methods in spectral geometry. This project aims to use methods from mathematical scattering theory to resolve problems in the spectral analysis and index theory of differential operators. Both areas underpin the theoretical understanding of physical materials at micro length scales where quantum phenomena dominate. The project will develop new mathematical results in spectral analysis and geometry, and apply its results to theoretical models of quantum phenomena whose spectral properties are ....New methods in spectral geometry. This project aims to use methods from mathematical scattering theory to resolve problems in the spectral analysis and index theory of differential operators. Both areas underpin the theoretical understanding of physical materials at micro length scales where quantum phenomena dominate. The project will develop new mathematical results in spectral analysis and geometry, and apply its results to theoretical models of quantum phenomena whose spectral properties are at the limit of the range of mathematical techniques. Solving these problems is expected to influence non-commutative analysis.Read moreRead less
Resilience of eucalypts to future droughts. This project aims to examine how resilient Eucalyptus species are to future droughts by combining data synthesis, manipulative experiments and modelling. Climate change is expected to increase the frequency, magnitude and duration of future droughts, with major environmental and socio-economic consequences for Australia. Current predictive capacity is extremely limited: experiments are limited in scale and cannot capture important global change interac ....Resilience of eucalypts to future droughts. This project aims to examine how resilient Eucalyptus species are to future droughts by combining data synthesis, manipulative experiments and modelling. Climate change is expected to increase the frequency, magnitude and duration of future droughts, with major environmental and socio-economic consequences for Australia. Current predictive capacity is extremely limited: experiments are limited in scale and cannot capture important global change interactions, whilst models do not represent the functional characteristics and adaptions of eucalypts. This project will develop a strong evidence- and process-based understanding to quantify the functional behaviour of drought-adapted Eucalyptus species and leverage this insight to make future model projections.Read moreRead less
A step change in modeling leaf respiration-photosynthesis relationships . This project aims to use innovative, high-throughput technologies to develop a novel framework that links daytime photosynthesis and starch/amino acid mobilisation to variations in night-time leaf respiration. Variations in leaf respiration can have large impacts on ecosystem functioning and the Earth’s climate. Although advances have been made in respiration modelling, current models are unable to predict dynamic, day-to- ....A step change in modeling leaf respiration-photosynthesis relationships . This project aims to use innovative, high-throughput technologies to develop a novel framework that links daytime photosynthesis and starch/amino acid mobilisation to variations in night-time leaf respiration. Variations in leaf respiration can have large impacts on ecosystem functioning and the Earth’s climate. Although advances have been made in respiration modelling, current models are unable to predict dynamic, day-to-day variations in respiratory rates. Expected outcomes include equations that predict daily variations in night-time leaf respiration for environments across Australia and overseas. Benefits to planners include the ability to more accurately model vegetation-atmosphere carbon exchange and future changes in climate. Read moreRead less