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
Solving and estimating dynamic models of strategic interaction. This project aims to investigate how firms interact with each other through time and how these interactions drive both the operation of, and value created in, economic markets. While recent theoretical models predominantly capture the complexity of these dynamic interactions, the methods for testing these models’ predictions against observed data do not. Instead, they are based on a range of simplifying assumptions that undermine th ....Solving and estimating dynamic models of strategic interaction. This project aims to investigate how firms interact with each other through time and how these interactions drive both the operation of, and value created in, economic markets. While recent theoretical models predominantly capture the complexity of these dynamic interactions, the methods for testing these models’ predictions against observed data do not. Instead, they are based on a range of simplifying assumptions that undermine the reliability of their analysis. This project will develop statistical and computational methods to better understand observed economic behaviour. By allowing the effects of proposed economic interventions and regulations ex ante, this project will support the development of more efficient and better-targeted policies in every area of the economy.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
Industrial Transformation Training Centres - Grant ID: IC220100035
Funder
Australian Research Council
Funding Amount
$4,958,927.00
Summary
ARC Training Centre for Hyphenated Analytical Separation Technologies . The toughest analytical science challenges typically require advanced analytical technologies to acquire the desired solutions. In the field of separation science this inevitably involves hyphenated separation technologies, specifically the combination of chromatography and mass spectrometry. Advancing this technology to its full capability requires the collaborative strength of academic, industry and end-user partnerships, ....ARC Training Centre for Hyphenated Analytical Separation Technologies . The toughest analytical science challenges typically require advanced analytical technologies to acquire the desired solutions. In the field of separation science this inevitably involves hyphenated separation technologies, specifically the combination of chromatography and mass spectrometry. Advancing this technology to its full capability requires the collaborative strength of academic, industry and end-user partnerships, providing the materials and inspiration for young researchers to apply novel hyphenated methods to complex environmental and industrial systems. This Centre will deliver fundamental developments in hyphenated technologies, new analytical capability, and applied outcomes across multiple end-user groups and interests. Read moreRead less
Targeting conjugated markers with new metabolomic methods. Detecting the illicit use of natural steroids like testosterone, or compounds that modulate natural steroid levels, remains the greatest challenge for drug testing in all forms of sport. This project aims to develop new metabolomic methods based on liquid chromatography-high resolution mass spectrometry to detect the changes occurring in the conjugated steroid profile following the administration of steroids or steroid modulators. The in ....Targeting conjugated markers with new metabolomic methods. Detecting the illicit use of natural steroids like testosterone, or compounds that modulate natural steroid levels, remains the greatest challenge for drug testing in all forms of sport. This project aims to develop new metabolomic methods based on liquid chromatography-high resolution mass spectrometry to detect the changes occurring in the conjugated steroid profile following the administration of steroids or steroid modulators. The intended outcome will be a set of sensitive and analytical methods using a range of newly identified conjugated steroid markers and associated reference materials, which promises to enhance integrity and animal welfare in the thoroughbred racing industry.
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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
Regularisation methods of inverse problems: theory and computation. This project aims to investigate regularisation methods for inverse problems which are ill-posed in the sense that their solutions depend discontinuously on the data. When only noisy data is available, regularisation methods define stable approximate solutions by replacing the original inverse problem with a family of well-posed neighbouring problems monitored by a so-called regularisation parameter. The project expects to devel ....Regularisation methods of inverse problems: theory and computation. This project aims to investigate regularisation methods for inverse problems which are ill-posed in the sense that their solutions depend discontinuously on the data. When only noisy data is available, regularisation methods define stable approximate solutions by replacing the original inverse problem with a family of well-posed neighbouring problems monitored by a so-called regularisation parameter. The project expects to develop purely data-driven rules to choose the regularisation parameter and show how they work in theory, and in practice. It will also develop convex framework, acceleration strategies as well as preconditioning and splitting ideas to design efficient regularisation solvers.Read moreRead less
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
Terahertz lasers in the fight against illicit substances. This project aims to investigate the application of cutting-edge terahertz laser technology with new spectroscopic methods, for detection of illicit substances. Using a collaborative approach, the project aims to bring together expertise in laser physics, spectroscopy, law enforcement and instrumentation, and seeks to develop new sources and detection protocols which will offer new capabilities to law enforcement, aiding in detection and ....Terahertz lasers in the fight against illicit substances. This project aims to investigate the application of cutting-edge terahertz laser technology with new spectroscopic methods, for detection of illicit substances. Using a collaborative approach, the project aims to bring together expertise in laser physics, spectroscopy, law enforcement and instrumentation, and seeks to develop new sources and detection protocols which will offer new capabilities to law enforcement, aiding in detection and identification protocols for illicit substances.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE190101382
Funder
Australian Research Council
Funding Amount
$325,000.00
Summary
Optimising digital mental health care: how technology is used in practice. This project aims to develop the first national consensus statement on the use of technology in mental health care in Australia. The project will examine how Australian health practitioners currently use digital therapy programs, and synthesise this data with international evidence and input from Australian government, health service, and digital health experts. This project expects to improve the implementation of digita ....Optimising digital mental health care: how technology is used in practice. This project aims to develop the first national consensus statement on the use of technology in mental health care in Australia. The project will examine how Australian health practitioners currently use digital therapy programs, and synthesise this data with international evidence and input from Australian government, health service, and digital health experts. This project expects to improve the implementation of digital therapy tools using an innovative, theory-driven approach. Expected outcomes of this project include increased and optimal implementation of digital therapy tools among mental health care providers and enhanced capacity within the Australian health system to meet the high demand for services in the community.Read moreRead less