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Discovery Early Career Researcher Award - Grant ID: DE130101670
Funder
Australian Research Council
Funding Amount
$370,410.00
Summary
Scalable Bayesian model selection for massive data sets. This project will develop highly innovative, efficient and ultimately effective methodology for Bayesian model selection for large-scale problems which commonly arise in biostatistics and bioinformatics. The resulting methodology will dramatically reduce the duration of analyses in these areas from days or weeks to minutes or hours.
Discovery Early Career Researcher Award - Grant ID: DE160101565
Funder
Australian Research Council
Funding Amount
$330,000.00
Summary
Flexible data modelling via skew mixture models:challenges and applications. This project seeks to explore new models for handling data with non-normal features. Parametric distributions are fundamental to statistical modelling and inference. For centuries, the ‘normal’ distribution has been the dominant model for continuous data. However, real data rarely satisfy the assumption of normality. There is thus a strong demand for more flexible distributions. This project aims to develop new methodol ....Flexible data modelling via skew mixture models:challenges and applications. This project seeks to explore new models for handling data with non-normal features. Parametric distributions are fundamental to statistical modelling and inference. For centuries, the ‘normal’ distribution has been the dominant model for continuous data. However, real data rarely satisfy the assumption of normality. There is thus a strong demand for more flexible distributions. This project aims to develop new methodologies in finite mixture modelling using skew component distributions to provide better models for handling data with non-normal features (such as skewness, heavy/light tails, and multimodality). Applications may include security intrusion detection, clinical diagnosis and prognosis, and flow and mass cytometry.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE240101190
Funder
Australian Research Council
Funding Amount
$451,000.00
Summary
Innovating and Validating Scalable Monte Carlo Methods. This project aims to develop innovative scalable Monte Carlo methods for statistical analysis in the presence of big data or complex mathematical models. Existing approaches to scalable Monte Carlo are only approximate, and their inaccuracies are difficult to quantify. This can have a detrimental impact on data-based decision making. The expected outcomes of this project are scalable Monte Carlo methods that are more accurate, fast and capa ....Innovating and Validating Scalable Monte Carlo Methods. This project aims to develop innovative scalable Monte Carlo methods for statistical analysis in the presence of big data or complex mathematical models. Existing approaches to scalable Monte Carlo are only approximate, and their inaccuracies are difficult to quantify. This can have a detrimental impact on data-based decision making. The expected outcomes of this project are scalable Monte Carlo methods that are more accurate, fast and capable of quantifying inaccuracies. Scientists and decision-makers will benefit from the ability to obtain timely, reliable insights for challenging applications.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE240100109
Funder
Australian Research Council
Funding Amount
$460,157.00
Summary
Sexual offence interviewing: Towards victim-survivor well-being and justice. This project aims to improve the way victim-survivors are interviewed in sexual offence cases by examining their experiences and perceptions of investigative interview techniques. It expects to generate new knowledge about interview techniques that can promote victim well-being and the disclosure of sensitive information during investigative interviews. Expected outcomes include new theoretical frameworks in the field o ....Sexual offence interviewing: Towards victim-survivor well-being and justice. This project aims to improve the way victim-survivors are interviewed in sexual offence cases by examining their experiences and perceptions of investigative interview techniques. It expects to generate new knowledge about interview techniques that can promote victim well-being and the disclosure of sensitive information during investigative interviews. Expected outcomes include new theoretical frameworks in the field of investigative interviewing and an innovative toolkit of victim-centred training resources to directly inform investigative interview policies and practices in sexual offence cases. Anticipated benefits include better victim experiences of investigative interviews and enhanced justice responses to sexual violence.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE140100993
Funder
Australian Research Council
Funding Amount
$293,520.00
Summary
Mathematics of importance: The optimal importance sampling algorithm for estimating the probability of a black swan event. Rare event simulation and modelling is critical to our understanding of high-cost hard-to-predict events such as nuclear accidents, natural disasters, and financial crises. Quantitative analysis of such high-impact events demands the accurate estimation of the probability of occurrence of such rare events. In realistic models this probability is very difficult to estimate, ....Mathematics of importance: The optimal importance sampling algorithm for estimating the probability of a black swan event. Rare event simulation and modelling is critical to our understanding of high-cost hard-to-predict events such as nuclear accidents, natural disasters, and financial crises. Quantitative analysis of such high-impact events demands the accurate estimation of the probability of occurrence of such rare events. In realistic models this probability is very difficult to estimate, because exact simple analytical formulas are not available and the existing estimation methods fail spectacularly. There is an urgent need for new efficient methodology. This project develops a new Monte Carlo method that will be able to estimate reliably and accurately rare-event probabilities. Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE160101147
Funder
Australian Research Council
Funding Amount
$294,336.00
Summary
Predicting extremes when events occur in bursts. This project seeks to advance knowledge in extreme value theory. Extreme value theory is essential to quantify risks in complex systems, such as the risk of network failures. Current statistical models for the occurrence of extremes assume that events happen regularly. This assumption, however, is at odds with human actions and many biological and physical events, which occur in bursts. There is a strong need to understand the effect of such ‘burs ....Predicting extremes when events occur in bursts. This project seeks to advance knowledge in extreme value theory. Extreme value theory is essential to quantify risks in complex systems, such as the risk of network failures. Current statistical models for the occurrence of extremes assume that events happen regularly. This assumption, however, is at odds with human actions and many biological and physical events, which occur in bursts. There is a strong need to understand the effect of such ‘bursty dynamics’ on the frequency and magnitude of extreme events. This project aims to develop extreme value theory for bursty events and thus lay the mathematical groundwork for the estimation and prediction of extremes in a variety of scientific contexts.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200101253
Funder
Australian Research Council
Funding Amount
$349,586.00
Summary
Making Machine Learning Fair(er). This project aims to develop and implement statistical methods to fight against algorithm bias. In doing so, this project expects to generate new knowledge in the mathematical sciences by employing innovative and interdisciplinary approaches to the development of fairness constraints on machine learning algorithms. Fairness will be seen through the lens of invariance, allowing the developed conceptual framework to find broad applications. Expected outcomes of t ....Making Machine Learning Fair(er). This project aims to develop and implement statistical methods to fight against algorithm bias. In doing so, this project expects to generate new knowledge in the mathematical sciences by employing innovative and interdisciplinary approaches to the development of fairness constraints on machine learning algorithms. Fairness will be seen through the lens of invariance, allowing the developed conceptual framework to find broad applications. Expected outcomes of this project include improved techniques for imposing invariance on deep learning algorithms. This should provide significant benefits to the general public by contributing to the advancement of socially responsible and conscientious machine learning.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE180101252
Funder
Australian Research Council
Funding Amount
$343,450.00
Summary
Statistical theory and algorithms for joint inference of complex networks. This project aims to address the challenges in jointly modelling complex networks by applying an integrated approach encompassing statistical theory, computation, and applications. The project expects to contribute to core statistical methodology development for complex inference and generate new knowledge in the fields of genomics, neuroscience, and social science through in-depth analyses of large-scale multilayered net ....Statistical theory and algorithms for joint inference of complex networks. This project aims to address the challenges in jointly modelling complex networks by applying an integrated approach encompassing statistical theory, computation, and applications. The project expects to contribute to core statistical methodology development for complex inference and generate new knowledge in the fields of genomics, neuroscience, and social science through in-depth analyses of large-scale multilayered network data. Expected outcomes include enhanced theoretical and computational frameworks for probabilistic network models to better utilise the power of multiple observations. This should foster international and interdisciplinary collaborations and add significant value to the rapidly progressing field of networks research.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE210100357
Funder
Australian Research Council
Funding Amount
$427,320.00
Summary
What determines your face identification accuracy? Accurate face identification underpins normal social functioning and important identity verification procedures in society, government and the justice system. However, there is little understanding of the cognitive processes that give rise to individual differences in face identification. This project aims to develop a new cognitive model that characterises how holistic and part-based processing combine to determine individual differences in fac ....What determines your face identification accuracy? Accurate face identification underpins normal social functioning and important identity verification procedures in society, government and the justice system. However, there is little understanding of the cognitive processes that give rise to individual differences in face identification. This project aims to develop a new cognitive model that characterises how holistic and part-based processing combine to determine individual differences in face identification. Expected benefits include advancing knowledge of human face perception, and evidence-based training and personnel selection tools to improve decision accuracy, help police prevent crime and terrorism, and avoid wrongful conviction of innocent suspects.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE170100748
Funder
Australian Research Council
Funding Amount
$360,000.00
Summary
Statistical tools for assessing effects of environmental change. This project aims to develop statistical tools for improving prediction of environmental exceedances, such as atmospheric carbon dioxide sources and sinks. Predicting extreme environmental conditions or events is crucial for effective environmental decision-making and management. The project will develop the tools using statistical inference based on a statistical model that combines predictions from related scientific models. In t ....Statistical tools for assessing effects of environmental change. This project aims to develop statistical tools for improving prediction of environmental exceedances, such as atmospheric carbon dioxide sources and sinks. Predicting extreme environmental conditions or events is crucial for effective environmental decision-making and management. The project will develop the tools using statistical inference based on a statistical model that combines predictions from related scientific models. In the case of carbon dioxide, improving prediction reliability by reducing bias and uncertainty whilst accounting for model-based dependence is an important step toward mitigating carbon dioxide sources and protecting carbon dioxide sinks. This capability is crucial for adaptive planning and a resilient society.Read moreRead less