Discovery Early Career Researcher Award - Grant ID: DE170100234
Funder
Australian Research Council
Funding Amount
$360,000.00
Summary
Exact and hybrid algorithms for the Aircraft Landing Problem. This project aims to develop algorithms with superior guaranteed performance. Aircraft Landing Problems (ALP) are an important class of decision problems. Optimal solution of an ALP is applicable in transportation and health care delivery, benefitting systems experiencing long delays. This project aims to address several of the Australian Government's Science and Research Priorities, focusing on food supply chains, effective operation ....Exact and hybrid algorithms for the Aircraft Landing Problem. This project aims to develop algorithms with superior guaranteed performance. Aircraft Landing Problems (ALP) are an important class of decision problems. Optimal solution of an ALP is applicable in transportation and health care delivery, benefitting systems experiencing long delays. This project aims to address several of the Australian Government's Science and Research Priorities, focusing on food supply chains, effective operation and resource allocation in transport, and better models of health care delivery and services.Read moreRead less
Advanced Mixture Models for the Analysis of Modern-Day Data. Extracting key information from huge data sets is critical to the scientific successes of the future. This project will develop novel mixture models that can be used directly to analyse complex and high-dimensional data sets that may consist of thousands of variables observed on only a limited number of entities. In order to handle the challenging problems arising in the latter situation. This project develops mixtures of factor models ....Advanced Mixture Models for the Analysis of Modern-Day Data. Extracting key information from huge data sets is critical to the scientific successes of the future. This project will develop novel mixture models that can be used directly to analyse complex and high-dimensional data sets that may consist of thousands of variables observed on only a limited number of entities. In order to handle the challenging problems arising in the latter situation. This project develops mixtures of factor models with options for skew distributions that can be used to effectively analyse such data. Key applications include the domains of bioinformatics, biostatistics, business, data mining, economics, finance, image analysis, marketing, and personalised medicine, among many others.Read moreRead less
Joint clustering and matching of multivariate samples across objects. The project will provide a novel and very effective approach to the clustering of multivariate samples on objects, say patients, that automatically matches the sample clusters across the objects. A key application is the matching of biologically relevant cell subtypes across patients for use in the study and the clinical diagnosis and prognosis of cancer.
Expanding the role of mixture models in statistical analyses of big data. This project aims to develop theoretical procedures to scale inference and learning algorithms to analyse big data sets. It will develop analytic tools and algorithms to analyse big data sets which classical methods of inference cannot analyse directly due to the data’s complexity or size. This will accelerate the progress of scientific discovery and innovation, leading, for example, to new fields of inquiry; to an increas ....Expanding the role of mixture models in statistical analyses of big data. This project aims to develop theoretical procedures to scale inference and learning algorithms to analyse big data sets. It will develop analytic tools and algorithms to analyse big data sets which classical methods of inference cannot analyse directly due to the data’s complexity or size. This will accelerate the progress of scientific discovery and innovation, leading, for example, to new fields of inquiry; to an increase in understanding from studies on human and social processes and interactions; and to the promotion of economic growth and improved health and quality of life. Such applications should lead to breakthrough discoveries and innovation in science, engineering, medicine, commerce, education and national security.Read moreRead less
A new approach to fast matrix factorization for the statistical analysis of high-dimensional data. Some form of dimension reduction is essential in order to extract meaningful information from huge data sets. For this purpose we provide a novel and very fast approach to the factorization of the data matrix. It has wide applicability for improving the quality and validity of research in science and medicine and in most industries in Australia.
Large-Scale Statistical Inference: Multiple Testing. Multiple testing procedures are among the most important statistical tools for the analysis of modern data. This project aims to develop new methods for providing more powerful simultaneous tests while controlling the proportion of false positive conclusions. They are proposed to be derived by the novel pooling of information in individual attribute based contrasts to produce a Weighted Individual attribute-Specific Contrast (WISC) based stati ....Large-Scale Statistical Inference: Multiple Testing. Multiple testing procedures are among the most important statistical tools for the analysis of modern data. This project aims to develop new methods for providing more powerful simultaneous tests while controlling the proportion of false positive conclusions. They are proposed to be derived by the novel pooling of information in individual attribute based contrasts to produce a Weighted Individual attribute-Specific Contrast (WISC) based statistic. They will also exploit contextual information. They are expected to be of direct application to the problem of testing for no differences between two or more classes, as in the detection of differential expression in bioinformatics. Other key applications are expected to include biomedicine, economics, finance, genetics, and neuroscience.Read moreRead less
Acquiring physical skills: exploiting games technology to teach sign language. This project will investigate if games technology can be used to teach deaf children’s parents sign language. The learner would create a sign, the system would assess the accuracy of the sign and provide feedback to improve learning. If successful, the system would provide an inexpensive alternative to learning sign language.
COMPLEX NETWORKS: DYNAMICS, OPTIMIZATION AND CONTROL. Complex networks such large power grids, the Internet, transportation networks and co-operation networks of all kinds provide challenges for frontier technologies particularly computing, communication and control. In particular, advanced societies have become dependent on large infrastructure networks to an extent beyond our capability to plan and control them. The recent spate of collapses in power grids and virus attacks on the Internet i ....COMPLEX NETWORKS: DYNAMICS, OPTIMIZATION AND CONTROL. Complex networks such large power grids, the Internet, transportation networks and co-operation networks of all kinds provide challenges for frontier technologies particularly computing, communication and control. In particular, advanced societies have become dependent on large infrastructure networks to an extent beyond our capability to plan and control them. The recent spate of collapses in power grids and virus attacks on the Internet illustrate the need for research on modelling, analysis of behaviour, planning and control in such networks. This project aims to establish research in this area for Australia's benefit.Read moreRead less
Dynamics and Security Control of Complex Networks. The research will yield basic techniques to analyse, design and operate complex networks so that security, as well as performance, is achieved. These techniques will be further developed towards particular applications including power grids and telecommunication networks. However, the emphasis is on providing basic ideas and techniques.
Construction of near optimal oscillatory regimes in singularly perturbed control systems via solutions of Hamilton-Jacobi-Bellman inequalities. Problems of optimal control of systems evolving in multiple time scales arise in a great variety of applications (from diet to environmental modelling). This project addresses the challenge of analytically and numerically constructing rapidly oscillating controls that would 'near optimally coordinate' the slow and fast dynamics.