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.
Discovery Early Career Researcher Award - Grant ID: DE150101655
Funder
Australian Research Council
Funding Amount
$297,036.00
Summary
Discriminative detection and quantification of cancer imaging biomarkers. This project aims to develop a new framework for the detection and quantification of cancer biomarkers in diagnostic and histopathology images with discriminative modelling of intrinsic structures. The framework will be the first computerised solution to provide automated, quantitative annotations of cancer imaging biomarkers at the macroscopic and microscopic levels to support standardised reporting of image interpretatio ....Discriminative detection and quantification of cancer imaging biomarkers. This project aims to develop a new framework for the detection and quantification of cancer biomarkers in diagnostic and histopathology images with discriminative modelling of intrinsic structures. The framework will be the first computerised solution to provide automated, quantitative annotations of cancer imaging biomarkers at the macroscopic and microscopic levels to support standardised reporting of image interpretation. It will help to alleviate the inter-observer variability and time-consuming process of manual analysis. The project aims to advance fundamental biomedical imaging research in generalised visual structure extraction and classification, and enable large-scale translational research in systems pathology for personalised cancer care.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE160101518
Funder
Australian Research Council
Funding Amount
$294,111.00
Summary
Multi-Object Recognition of Biomedical Images via Holistic Ontology. This project seeks to advance the development of new biomedical image recognition and analysis solutions by associating biomedical images with biomedical knowledge and personalised data. The provision of accurate and robust multi-object recognition and analysis from biomedical image data is a fundamental requirement for biomedical imaging applications. This project aims to improve the recognition and analysis of anatomical and ....Multi-Object Recognition of Biomedical Images via Holistic Ontology. This project seeks to advance the development of new biomedical image recognition and analysis solutions by associating biomedical images with biomedical knowledge and personalised data. The provision of accurate and robust multi-object recognition and analysis from biomedical image data is a fundamental requirement for biomedical imaging applications. This project aims to improve the recognition and analysis of anatomical and functional structures from biomedical images with ‘holistic ontology’ modelling that represents a multi-level biological, physiological, and anatomical knowledge base. The project will potentially have application in many health care areas, such as computer aided diagnosis, image-guided surgery planning, and image-based disease modelling.Read moreRead less
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.
Biomedical Visual Image Analytics for Multi-disciplinary Retrieval. The project aims to develop a framework to provide users with the interactive access to information that is necessary for the best collaborative decision-making. Visual analytics theory is becoming increasing valuable for managing ‘big data’ because it can provide interactive and intuitive understanding of the rich information embedded within complex data and decision support systems. There are, however, fundamental challenges t ....Biomedical Visual Image Analytics for Multi-disciplinary Retrieval. The project aims to develop a framework to provide users with the interactive access to information that is necessary for the best collaborative decision-making. Visual analytics theory is becoming increasing valuable for managing ‘big data’ because it can provide interactive and intuitive understanding of the rich information embedded within complex data and decision support systems. There are, however, fundamental challenges that currently prevent visual analytics from being routinely applied to multi-disciplinary collaboration, which is now ‘the norm’ to solve large complicated problems where there is significant social impact. This project aims to address these challenges and improve visual analytics theory by developing a biomedical visual image analytics framework that enables interactive information retrieval of multidisciplinary databases.Read moreRead less
Multiscale integration of imaging and omics data. This project aims to integrate multiscale imaging and molecular data to characterise disease in patients. Modern healthcare needs to embrace ‘big (health) data’s potential to address an ageing population’s increasing healthcare demands and the inefficiencies and waste in patient treatment. This project expects to pioneer basic science research in methodologies to integrate, correlate and then derive knowledge from multi-scale data, to characteris ....Multiscale integration of imaging and omics data. This project aims to integrate multiscale imaging and molecular data to characterise disease in patients. Modern healthcare needs to embrace ‘big (health) data’s potential to address an ageing population’s increasing healthcare demands and the inefficiencies and waste in patient treatment. This project expects to pioneer basic science research in methodologies to integrate, correlate and then derive knowledge from multi-scale data, to characterise the mechanisms of disease in individual patients, in space and time. Its integrated model is expected to form the basis of a framework for individualised patient disease analysis.Read moreRead less
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.