Discovery Early Career Researcher Award - Grant ID: DE200101253
Funder
Australian Research Council
Funding Amount
$349,586.00
Summary
Making Machine Learning Fair(er). This project aims to develop and implement statistical methods to fight against algorithm bias. In doing so, this project expects to generate new knowledge in the mathematical sciences by employing innovative and interdisciplinary approaches to the development of fairness constraints on machine learning algorithms. Fairness will be seen through the lens of invariance, allowing the developed conceptual framework to find broad applications. Expected outcomes of t ....Making Machine Learning Fair(er). This project aims to develop and implement statistical methods to fight against algorithm bias. In doing so, this project expects to generate new knowledge in the mathematical sciences by employing innovative and interdisciplinary approaches to the development of fairness constraints on machine learning algorithms. Fairness will be seen through the lens of invariance, allowing the developed conceptual framework to find broad applications. Expected outcomes of this project include improved techniques for imposing invariance on deep learning algorithms. This should provide significant benefits to the general public by contributing to the advancement of socially responsible and conscientious machine learning.Read moreRead less
Australian Laureate Fellowships - Grant ID: FL150100150
Funder
Australian Research Council
Funding Amount
$2,413,112.00
Summary
Bayesian learning for decision making in the big data era. Bayesian learning for decision making in the big data era: This fellowship project aims to develop new techniques in evidence-based learning and decision-making in the big data era. Big data has arrived, and with it a huge global demand for statistical knowledge and skills to analyse these data for improved learning and decision-making. This project will seek to address this need by creating a step-change in knowledge in Bayesian statist ....Bayesian learning for decision making in the big data era. Bayesian learning for decision making in the big data era: This fellowship project aims to develop new techniques in evidence-based learning and decision-making in the big data era. Big data has arrived, and with it a huge global demand for statistical knowledge and skills to analyse these data for improved learning and decision-making. This project will seek to address this need by creating a step-change in knowledge in Bayesian statistics and translating this knowledge to real-world challenges in industry, environment and health. The new big data statistical analysts trained through the project could also create much needed capacity at national and international levels.Read moreRead less
Inverse and related problems in statistics. Modern statistical inverse problems arise in fields from astronomy and biology to engineering and finance. Sometimes the problems involve the analysis of small samples of very high dimensional data, and are central to information aquisition in areas such as genomics and signal analysis. All these topics are of significant national importance, and their solution will bring national and community benefits. In addition, the program to which the proposa ....Inverse and related problems in statistics. Modern statistical inverse problems arise in fields from astronomy and biology to engineering and finance. Sometimes the problems involve the analysis of small samples of very high dimensional data, and are central to information aquisition in areas such as genomics and signal analysis. All these topics are of significant national importance, and their solution will bring national and community benefits. In addition, the program to which the proposal will lead will be used extensively for research training. In Australia, where the demand for research-trained statisticians greatly exceeds supply, this contribution to the nation and the community will be particularly important. Read moreRead less
New and computationally feasible methods of constructing efficient and exact confidence limits from count data. Biological and health science data is commonly in the form of counts. The statistical analysis of such data should be (a) efficient i.e. it should not, in effect, throw away valuable data, (b) exact i.e. it should have precisely known statistical properties and (c) computationally feasible. Kabaila and Lloyd (1997-2001) have proposed and analysed a radically new method of confidence li ....New and computationally feasible methods of constructing efficient and exact confidence limits from count data. Biological and health science data is commonly in the form of counts. The statistical analysis of such data should be (a) efficient i.e. it should not, in effect, throw away valuable data, (b) exact i.e. it should have precisely known statistical properties and (c) computationally feasible. Kabaila and Lloyd (1997-2001) have proposed and analysed a radically new method of confidence limit construction which, for the first time, possesses all of these requirements. The purpose of the project is to establish further theoretical support for the new method, to develop efficient computational algorithms and to write easy-to-use computer programs for its practical use.Read moreRead less
Classification methods for providing personalised and class decisions. This project provides a novel approach to the clustering of multivariate samples on entities in a class that automatically matches the sample clusters across the entities, allowing for inter-sample variation between the samples in a class. The project aims to develop a widely applicable, mixture-model-based framework for the simultaneous clustering of multivariate samples with inter-sample variation in a class and for the mat ....Classification methods for providing personalised and class decisions. This project provides a novel approach to the clustering of multivariate samples on entities in a class that automatically matches the sample clusters across the entities, allowing for inter-sample variation between the samples in a class. The project aims to develop a widely applicable, mixture-model-based framework for the simultaneous clustering of multivariate samples with inter-sample variation in a class and for the matching of the clusters across the entities in the class. The project will use a statistical approach to automatically match the clusters, since the overall mixture model provides a template for the class. It will provide a basis for discriminating between different classes in addition to the identification of atypical data points within a sample and of anomalous samples within a class. Key applications include biological image analysis and the analysis of data in flow cytometry which is one of the fundamental research tools for the life scientist.Read moreRead less
Industrial Transformation Training Centres - Grant ID: IC190100031
Funder
Australian Research Council
Funding Amount
$3,973,202.00
Summary
ARC Training Centre in Data Analytics for Resources and Environments (DARE). Understanding the cumulative impact of actions regarding the use of our resources has important long-term consequences for Australia’s economic, societal and environmental health. Yet despite the importance of these cumulative impacts, and the availability of data, many decisions and policies are based on limited amounts of data and rudimentary data analysis, with little appreciation of the critical role that understand ....ARC Training Centre in Data Analytics for Resources and Environments (DARE). Understanding the cumulative impact of actions regarding the use of our resources has important long-term consequences for Australia’s economic, societal and environmental health. Yet despite the importance of these cumulative impacts, and the availability of data, many decisions and policies are based on limited amounts of data and rudimentary data analysis, with little appreciation of the critical role that understanding and quantifying uncertainty plays in the process. The aim of Data Analytics in Resources and Environment (DARE) is to develop and deliver the data science skills and tools for Australia’s resource industries to make the best possible evidence-based decisions in exploiting and stewarding the nation’s natural resources.Read moreRead less
Statistical methods for quantifying variation in spatiotemporal areal data. This project aims to develop new statistical methods for extracting insights into spatial and temporal variation in areal data. These tools will extend the Australian Cancer Atlas which provides small area estimates for 20 cancers across Australia. The project is significant because it will allow government and other organisations to reap dividends from investment in collecting spatial information and it will enable mode ....Statistical methods for quantifying variation in spatiotemporal areal data. This project aims to develop new statistical methods for extracting insights into spatial and temporal variation in areal data. These tools will extend the Australian Cancer Atlas which provides small area estimates for 20 cancers across Australia. The project is significant because it will allow government and other organisations to reap dividends from investment in collecting spatial information and it will enable modelled small-area estimates to be released without compromising confidentiality. The expected outcomes include new statistical knowledge and new insights into cancer. The results will benefit the many disciplines, managers and policy makers that make decisions based on geographic data mapped over space and time. Read moreRead less
Information theoretic approaches to optimise genome wide association studies with application to continuous and discrete traits. This project aims to develop new mathematical methods to find genetic associations from new genome-wide studies of colorectal cancer and breast cancer risk factors. If successful, this will result in improved use of expensive genetic data to better predict and understand diseases, conditions and other characteristics for humans, animals and plants.
Theory and Applications of Computer-Intensive Statistical Methods. The availability of powerful computing equipment has had a dramatic impact on statistical methods and thinking. It has motivated development of novel approaches to data analysis, whose conception
and appreciation, even their application, often demand sophisticated and complex theoretical methods. In this context, the project will develop new approaches to solving non-standard statistical problems. These techniques will eithe ....Theory and Applications of Computer-Intensive Statistical Methods. The availability of powerful computing equipment has had a dramatic impact on statistical methods and thinking. It has motivated development of novel approaches to data analysis, whose conception
and appreciation, even their application, often demand sophisticated and complex theoretical methods. In this context, the project will develop new approaches to solving non-standard statistical problems. These techniques will either have direct application to solving practical problems of national or community concern, or provide a better understanding of the nature of such problems.Read moreRead less
Theory and application of computer-intensive, nonparametric statistical methods. The availability of increasingly powerful computing equipment continues to have a dramatic impact on statistical methods and thinking. These developments, combined with new technologies for generating data, are driving substantial changes in statistics, ranging from the types of problems being solved to the sorts of methods used to solve them. Both the problems and their solutions are of substantial national and c ....Theory and application of computer-intensive, nonparametric statistical methods. The availability of increasingly powerful computing equipment continues to have a dramatic impact on statistical methods and thinking. These developments, combined with new technologies for generating data, are driving substantial changes in statistics, ranging from the types of problems being solved to the sorts of methods used to solve them. Both the problems and their solutions are of substantial national and community benefit. They will be the subject of high-level research supported by this proposal. Read moreRead less