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Field of Research : Applied Statistics
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  • Funded Activity

    ARC Centres Of Excellence - Grant ID: CE140100049

    Funder
    Australian Research Council
    Funding Amount
    $20,000,000.00
    Summary
    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.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP200100747

    Funder
    Australian Research Council
    Funding Amount
    $520,000.00
    Summary
    Optimising progress towards elimination of malaria. The project aims to advance mathematical knowledge by developing novel tools appropriate for modelling disease elimination. We will apply these new mathematical tools to the significant problem of malaria elimination in Vietnam. The expected outcomes are new tools for modelling disease elimination on a fine spatial resolution with heterogeneities in individual patient characteristics, calibrating models to household level data on disease transm .... Optimising progress towards elimination of malaria. The project aims to advance mathematical knowledge by developing novel tools appropriate for modelling disease elimination. We will apply these new mathematical tools to the significant problem of malaria elimination in Vietnam. The expected outcomes are new tools for modelling disease elimination on a fine spatial resolution with heterogeneities in individual patient characteristics, calibrating models to household level data on disease transmission and designing intervention strategies for maximum effect on disease transmission. The innovative combination of modelling, inference and optimisation ensures that the mathematical methods developed will be broadly applicable to modelling elimination strategies for other infectious diseases.
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    Funded Activity

    Australian Laureate Fellowships - Grant ID: FL140100012

    Funder
    Australian Research Council
    Funding Amount
    $2,830,000.00
    Summary
    Stress-testing algorithms: generating new test instances to elicit insights. Stress-testing algorithms: generating new test instances to elicit insights. This project aims to develop a new paradigm in algorithm testing, creating novel test instances and tools to elicit insights into algorithm strengths and weaknesses. Such advances are urgently needed to support good research practice in academia, and to avoid disasters when deploying algorithms in practice. Extending our recent work in algorith .... Stress-testing algorithms: generating new test instances to elicit insights. Stress-testing algorithms: generating new test instances to elicit insights. This project aims to develop a new paradigm in algorithm testing, creating novel test instances and tools to elicit insights into algorithm strengths and weaknesses. Such advances are urgently needed to support good research practice in academia, and to avoid disasters when deploying algorithms in practice. Extending our recent work in algorithm testing for combinatorial optimisation, described as 'ground-breaking,' this project aims to tackle the challenges needed to generalise the paradigm to other fields such as machine learning, forecasting, software testing, and other branches of optimisation. An online repository of test instances and tools aim to provide a valuable resource to improve research practice and support new insights into algorithm performance.
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    Showing 1-3 of 3 Funded Activites

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