Devising tools for big data sets to support computational movement analysis. This project aims to devise practical fundamental algorithms and multi-purpose data structures with performance guarantees for big spatio-temporal data sets. Systematic analysis of trajectory data has been occurring since the 1950s, but with the recent technological advances the size of the data sets has recently soared. Existing computational tools were developed for small to mid-size data sets. This project aims to d ....Devising tools for big data sets to support computational movement analysis. This project aims to devise practical fundamental algorithms and multi-purpose data structures with performance guarantees for big spatio-temporal data sets. Systematic analysis of trajectory data has been occurring since the 1950s, but with the recent technological advances the size of the data sets has recently soared. Existing computational tools were developed for small to mid-size data sets. This project aims to devise practical fundamental algorithms that will enable the development of domain specific tools for a wide range of applications, including sports, behavioural ecology, transport, and surveillance.Read moreRead less
Next-generation techniques for analysing massive data sets. To process enormous amounts of data, leading computing companies are turning to modern computing frameworks, for which little theory of efficient computational techniques has been developed. This project will resolve key theoretical questions and provide fast techniques for poorly understood pattern recognition and bioinformatics problems.
A probabilistic framework for nonlinear dimensionality reduction algorithms. The Twin Measures Framework is a novel platform for analysing existing dimensionality reduction methods and the invention of new ones. This research will radically improve image analysis, with beneficial applications from pharmaceutical drug design through to border protection.
Mining multi-typed and dynamic graphs. Large volumes of data collected nowadays from real-world applications are often represented as graphs. The nodes and the edges of such graphs represent different types of entities and interactions, and they have time information. This project will develop algorithms that mine efficiently such multi-typed and dynamic graphs.
Novel data mining techniques for complex network analysis and control. This project will develop novel data mining theories and algorithms to analyse complex networks for safe information publishing and sharing across networks. It will enable smart information use in bioinformatics, social science and business intelligence, help protect against cybercrime and promote Australia's international research profile.
Australian Laureate Fellowships - Grant ID: FL110100281
Funder
Australian Research Council
Funding Amount
$2,777,066.00
Summary
Large-scale statistical machine learning. This research program aims to develop the science behind statistical decision problems as varied as web retrieval, genomic data analysis and financial portfolio optimisation. Advances will have a very significant practical impact in the many areas of science and technology that need to make sense of large, complex data streams.