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
Generalised Degrees of Freedom and Probabilistic Regularisation. This project intends to develop novel statistical tools for more accurate prediction by taking account of model complexity and uncertainties associated with the fitting procedure. The project also plans to develop a novel shrinkage approach via new penalty functions to avoid over-fitting and asymptotic properties. The key applications may include genetic studies where the number of predictors is large and biological experiments whe ....Generalised Degrees of Freedom and Probabilistic Regularisation. This project intends to develop novel statistical tools for more accurate prediction by taking account of model complexity and uncertainties associated with the fitting procedure. The project also plans to develop a novel shrinkage approach via new penalty functions to avoid over-fitting and asymptotic properties. The key applications may include genetic studies where the number of predictors is large and biological experiments where multivariate and temporal data are often collected – for example economical breeding in animal and fish farming and more effectively detecting the genes of interest in genetic studies on human, animals and plants.Read moreRead less
Novel statistical methods for data with non-Euclidean geometric structure. This project aims to develop new flexible regression models and classification algorithms, along with robust and efficient inference methods, applicable to a wide range of non-Euclidean data types which arise in many fields of science, business and technology. There are serious flaws with currently available methods of analysis for non-Euclidean data. This project expects to transform such analyses by providing new quanti ....Novel statistical methods for data with non-Euclidean geometric structure. This project aims to develop new flexible regression models and classification algorithms, along with robust and efficient inference methods, applicable to a wide range of non-Euclidean data types which arise in many fields of science, business and technology. There are serious flaws with currently available methods of analysis for non-Euclidean data. This project expects to transform such analyses by providing new quantitative tools within a unifying framework. The anticipated project outcomes will be of mathematical interest and valuable in applications such as finance (predicting Australian stock returns); modelling electroencephalography data; Australian geochemical data, relating to sediments; and Australian X-ray tumour image data. Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE190101326
Funder
Australian Research Council
Funding Amount
$391,546.00
Summary
Statistical methods for modelling the pathways between cause and effect. This project aims to develop new biostatistical methods for addressing complex analytic questions that arise in studies of the causes of health, social, educational and other outcomes in the course of human life. These questions concern the pathways that explain how intermediate factors contribute to a statistical relationship between a probable cause of a later outcome. Mathematical and statistical innovation is needed to ....Statistical methods for modelling the pathways between cause and effect. This project aims to develop new biostatistical methods for addressing complex analytic questions that arise in studies of the causes of health, social, educational and other outcomes in the course of human life. These questions concern the pathways that explain how intermediate factors contribute to a statistical relationship between a probable cause of a later outcome. Mathematical and statistical innovation is needed to address them. The expected outcomes include a suite of novel methods designed to evaluate the impact of intervening to modify causal pathways, while also accommodating common complexities of data such as incompleteness. This project should provide major benefits to studies in public health, social sciences and economics.Read moreRead less
High Predictive Performance Models via Semi-Parametric Survival Regression. This project will develop novel statistical models for high prediction performance. When applied to help doctor to treat patients, these models allow the users to include gene or other biomarkers for predicting effectiveness of a treatment. When applied to risk management in finance, these models are capable to include an organization's or individual's ongoing finance status to predict, for example, the probability of or ....High Predictive Performance Models via Semi-Parametric Survival Regression. This project will develop novel statistical models for high prediction performance. When applied to help doctor to treat patients, these models allow the users to include gene or other biomarkers for predicting effectiveness of a treatment. When applied to risk management in finance, these models are capable to include an organization's or individual's ongoing finance status to predict, for example, the probability of or time to loan default. Innovative computational methods will be developed for fitting these models. Compared to traditional prediction method, this approach allows greater flexibility while being superior in terms of statistical accuracy and bias. Extensive analyses of healthcare data from diverse fields will be undertaken.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200100944
Funder
Australian Research Council
Funding Amount
$427,068.00
Summary
Statistical frameworks for high-parameter imaging cytometry data. The project aims to develop statistical and bioinformatics methodology for characterising the complex interactions between cells in their native environment. Recent advances in imaging cytometry technologies have made it possible to observe the behaviour of multiple cell-types in tissue concurrently. The intended outcome is a suite of statistical methodologies that are crucial for addressing a variety of biological problems with t ....Statistical frameworks for high-parameter imaging cytometry data. The project aims to develop statistical and bioinformatics methodology for characterising the complex interactions between cells in their native environment. Recent advances in imaging cytometry technologies have made it possible to observe the behaviour of multiple cell-types in tissue concurrently. The intended outcome is a suite of statistical methodologies that are crucial for addressing a variety of biological problems with these state-of-the-art technologies. This work will advance knowledge in bioinformatics, statistics and image analysis, providing benefits to scientists studying the fundamental behaviour of cells and underlying disease mechanisms.Read moreRead less