Bayesian statistical models for understanding outcomes and improving decision-making for women screened for breast cancer. This project has two key benefits: (i) the development of frontier statistical methods for spatio-temporal analysis and data synthesis, which are imperative in a wide range of disciplines; and (ii) the application of these methods for improved understanding of breast cancer outcomes for women screened in Queensland. The project results will lead to direct health and financi ....Bayesian statistical models for understanding outcomes and improving decision-making for women screened for breast cancer. This project has two key benefits: (i) the development of frontier statistical methods for spatio-temporal analysis and data synthesis, which are imperative in a wide range of disciplines; and (ii) the application of these methods for improved understanding of breast cancer outcomes for women screened in Queensland. The project results will lead to direct health and financial benefits through targeted policies for increasing screening uptake and reducing cancer morbidity and mortality and therefore health spending in this area. Importantly, the project represents an excellent training opportunity to develop a PhD candidate into an experienced interdisciplinary researcher.Read moreRead less
Time consistency, risk-mitigation and partially observable systems. This project aims to find optimal decision rules that mitigate risk in a time consistent manner for partially observable systems. Many problems in conservation management and engineering systems are dependent on random environments and entail risk of failure. The challenge of consistently minimising such a risk while achieving satisfactory and sustainable resource consumption is considerable. This project aims to develop analyti ....Time consistency, risk-mitigation and partially observable systems. This project aims to find optimal decision rules that mitigate risk in a time consistent manner for partially observable systems. Many problems in conservation management and engineering systems are dependent on random environments and entail risk of failure. The challenge of consistently minimising such a risk while achieving satisfactory and sustainable resource consumption is considerable. This project aims to develop analytical and numerical methods for optimal control in such scenarios. These methods will have application to fishery management, communication networks, power systems and social resource allocation scenarios.Read moreRead less
Large Markov decision processes and combinatorial optimisation. Markov decision processes continue to gain in popularity for modelling a wide range of applications ranging from analysis of supply chains and queueing networks to cognitive science and control of autonomous vehicles. Nonetheless, they tend to become numerically intractable as the size of the model grows fast. Recent works use machine learning techniques to overcome this crucial issue, but with no convergence guarantee. This project ....Large Markov decision processes and combinatorial optimisation. Markov decision processes continue to gain in popularity for modelling a wide range of applications ranging from analysis of supply chains and queueing networks to cognitive science and control of autonomous vehicles. Nonetheless, they tend to become numerically intractable as the size of the model grows fast. Recent works use machine learning techniques to overcome this crucial issue, but with no convergence guarantee. This project aims to provide theoretically sound frameworks for solving large Markov decision processes, and exploit them to solve important combinatorial optimisation problems. This timely project can promote Australia's position in the development of such novel frameworks for many scientific and industrial applications.Read moreRead less
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE0453870
Funder
Australian Research Council
Funding Amount
$102,900.00
Summary
Social science advanced data modelling, analysis and visualisation facility. This is an integrated facility for advanced social science data analysis, modelling and visualisation located at The University of Queensland. The collaborating institutions are UQ, ANU and Griffith University. The facility promotes deep collaborations between social scientists and quantitative methodologists (statisticians, biostatisticians, econometricians) to enable leading edge quantitative analyses of survey, spati ....Social science advanced data modelling, analysis and visualisation facility. This is an integrated facility for advanced social science data analysis, modelling and visualisation located at The University of Queensland. The collaborating institutions are UQ, ANU and Griffith University. The facility promotes deep collaborations between social scientists and quantitative methodologists (statisticians, biostatisticians, econometricians) to enable leading edge quantitative analyses of survey, spatial and population data, new developments in quantitative methodology for statistical modelling and data visualisation, enhanced international research linkages and advanced postgraduate research training.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE130100291
Funder
Australian Research Council
Funding Amount
$374,595.00
Summary
Adaptive control of stochastic queueing networks. Queues of items competing for service appear on the road, in health-care, in manufacturing and in communication systems. This project will set up methodology for adaptive control and resource allocation for stochastic queueing network models applicable to a variety of scenarios accounting for parameter uncertainty.
Revolutionising water-quality monitoring in the information age. In today’s information age, automated low-cost sensors distributed in the environment have the potential to revolutionise the way we monitor and manage air, water and soil. This project aims to develop novel statistical methods to detect anomalies in the data generated from these in-situ sensors with computationally efficient modelling on river networks through space and time, with the applied goals of automating anomaly detection ....Revolutionising water-quality monitoring in the information age. In today’s information age, automated low-cost sensors distributed in the environment have the potential to revolutionise the way we monitor and manage air, water and soil. This project aims to develop novel statistical methods to detect anomalies in the data generated from these in-situ sensors with computationally efficient modelling on river networks through space and time, with the applied goals of automating anomaly detection in water-quality data and generating predictions of sediment and nutrient concentrations throughout river networks in near-real time. This will represent a fundamental increase in scientific knowledge, which will be immediately useful in the domains of aquatic science, environmental monitoring, and statistics.Read moreRead less
Congestion recovery and optimisation of patient flows. Australian public hospitals often experience congestion due to growing demand and limited resources, resulting in disruptions in service delivery and risks in quality of care. This project will apply advanced techniques and methodologies from mathematical sciences and computer modelling to alleviate this important healthcare delivery problem.
ARC Centre of Excellence for Mathematical and Statistical Frontiers of Big Data, Big Models, New Insights. In today's world, massive amounts of data in a variety of forms are collected daily from a multitude of sources. Many of the resulting data sets have the potential to make vital contributions to society, business and government, as well as impact on international developments, but are so large or complex that they are difficult to process and analyse using traditional tools. The aim of this ....ARC Centre of Excellence for Mathematical and Statistical Frontiers of Big Data, Big Models, New Insights. In today's world, massive amounts of data in a variety of forms are collected daily from a multitude of sources. Many of the resulting data sets have the potential to make vital contributions to society, business and government, as well as impact on international developments, but are so large or complex that they are difficult to process and analyse using traditional tools. The aim of this Centre is to create innovative mathematical and statistical models that can uncover the knowledge concealed within the size and complexity of these big data sets, with a focus on using the models to deliver insight into problems vital to the Centre's Collaborative Domains: Healthy People, Sustainable Environments and Prosperous Societies.Read moreRead less