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Field of Research : Computer-Human Interaction
Research Topic : Diagnositc algorithms
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  • Funded Activity

    Discovery Projects - Grant ID: DP110104937

    Funder
    Australian Research Council
    Funding Amount
    $300,000.00
    Summary
    Algorithms for geometric Turán-type problems and network visualization. Recent technological advances have large data sets, in a data deluge. Some of the most critical data sets are networks; examples abound in Systems Biology, Social Network Analysis, and Software Engineering. This project aims for algorithms to construct readable pictures of these networks, and thus make the data easier for humans to understand.
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    Funded Activity

    Discovery Projects - Grant ID: DP0988838

    Funder
    Australian Research Council
    Funding Amount
    $263,000.00
    Summary
    Scalable Visual Analytics for Uncertain Dynamic Networks. Technological advances have provided a data deluge over the past few years, and have led to many large uncertain and dynamic network models. This includes terrorist networks, marketing networks, facebook networks, various biological networks, and software engineering structures. Human understanding of such networks is difficult. This project aims to provide new methods for visual analysis of large uncertain dynamic networks such as these. .... Scalable Visual Analytics for Uncertain Dynamic Networks. Technological advances have provided a data deluge over the past few years, and have led to many large uncertain and dynamic network models. This includes terrorist networks, marketing networks, facebook networks, various biological networks, and software engineering structures. Human understanding of such networks is difficult. This project aims to provide new methods for visual analysis of large uncertain dynamic networks such as these. The algorithms developed in the project will help security analysts to monitor illegal behaviour such as money laundering and terrorist activities, help biologists understand key biological systems, and help engineers to understand large software systems.
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    Funded Activity

    Discovery Projects - Grant ID: DP0985271

    Funder
    Australian Research Council
    Funding Amount
    $230,000.00
    Summary
    An Integrative and Interactive Approach for Co-estimation of Multiple Sequence Alignment and Phylogeny Reconstruction. In this project innovative IT methods will be developed to assist biologists to solve complex and important biological problems. Many important applications in computational biology need very accurate and reliable tools for multiple sequence alignment and phylogeny reconstruction. Unfortunately, current existing tools are unreliable and are prone to serious errors when applied t .... An Integrative and Interactive Approach for Co-estimation of Multiple Sequence Alignment and Phylogeny Reconstruction. In this project innovative IT methods will be developed to assist biologists to solve complex and important biological problems. Many important applications in computational biology need very accurate and reliable tools for multiple sequence alignment and phylogeny reconstruction. Unfortunately, current existing tools are unreliable and are prone to serious errors when applied to large and divergent biological sequences. The success of this project will not only make significant contribution to the relevant research fields, but also help achieve goals in certain real-life biological research projects which are unique and important to Australia.
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    Funded Activity

    Linkage Projects - Grant ID: LP160100935

    Funder
    Australian Research Council
    Funding Amount
    $382,000.00
    Summary
    Visual analytics for massive multivariate networks. Visual analytics for massive multivariate networks. This project aims to create methods to visually analyse massive multivariate networks. The amount of network data available has exploded in recent years: software systems, social networks and biological systems have millions of nodes and billions of edges with multivariate attributes. Their size and complexity makes these data sets hard to exploit. More efficient ways to understand the data ar .... Visual analytics for massive multivariate networks. Visual analytics for massive multivariate networks. This project aims to create methods to visually analyse massive multivariate networks. The amount of network data available has exploded in recent years: software systems, social networks and biological systems have millions of nodes and billions of edges with multivariate attributes. Their size and complexity makes these data sets hard to exploit. More efficient ways to understand the data are needed. This project will design, implement and evaluate visualisation methods for massive multivariate network data sets. This research is expected to be used by Australian software development, biotechnology and security companies to exploit their data.
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    Funded Activity

    Linkage Projects - Grant ID: LP110100577

    Funder
    Australian Research Council
    Funding Amount
    $105,000.00
    Summary
    Visual analytics for high volume multi attribute financial data streams. While our ability to accumulate data (such as financial data) is increasing, our capability to analyse them is still inadequate despite technological improvements. The new Visual Analytics methods will allow processing of the massive and time-varying data so that the time-critical decisions can be made with minimum effort.
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    Showing 1-5 of 5 Funded Activites

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