Discovery Early Career Researcher Award - Grant ID: DE240101190
Funder
Australian Research Council
Funding Amount
$451,000.00
Summary
Innovating and Validating Scalable Monte Carlo Methods. This project aims to develop innovative scalable Monte Carlo methods for statistical analysis in the presence of big data or complex mathematical models. Existing approaches to scalable Monte Carlo are only approximate, and their inaccuracies are difficult to quantify. This can have a detrimental impact on data-based decision making. The expected outcomes of this project are scalable Monte Carlo methods that are more accurate, fast and capa ....Innovating and Validating Scalable Monte Carlo Methods. This project aims to develop innovative scalable Monte Carlo methods for statistical analysis in the presence of big data or complex mathematical models. Existing approaches to scalable Monte Carlo are only approximate, and their inaccuracies are difficult to quantify. This can have a detrimental impact on data-based decision making. The expected outcomes of this project are scalable Monte Carlo methods that are more accurate, fast and capable of quantifying inaccuracies. Scientists and decision-makers will benefit from the ability to obtain timely, reliable insights for challenging applications.Read moreRead less
Solving the solvent problem in chemical modelling. This project aims to produce highly accurate, user-friendly chemical solvent models using interdisciplinary theoretical chemistry techniques. The benefits of these novel models are extremely broad since chemical modelling is more impactful than traditional laboratory based techniques in solving multi-faceted modern chemical problems. The proposed outcomes of the project are significant, as they will transform how applied research solves difficul ....Solving the solvent problem in chemical modelling. This project aims to produce highly accurate, user-friendly chemical solvent models using interdisciplinary theoretical chemistry techniques. The benefits of these novel models are extremely broad since chemical modelling is more impactful than traditional laboratory based techniques in solving multi-faceted modern chemical problems. The proposed outcomes of the project are significant, as they will transform how applied research solves difficult and expensive real world chemical problems by allowing researchers to reliably include solvents in their models. It will have economic benefits for the chemical, mining and materials sectors in Australia, which represent billion-dollar industries.Read moreRead less
Australian Laureate Fellowships - Grant ID: FL190100080
Funder
Australian Research Council
Funding Amount
$3,432,323.00
Summary
New frontiers for nonequilibrium systems. The universe is comprised of systems in states of change or responding to a driving force. Yet a fundamental understanding of these nonequilibrium systems that enables predictive design has eluded scientists to date. This program aims to develop ground-breaking principles and methodologies to predict properties of nonequilibrium systems using both statistical physics and molecular simulations. Significantly, by pioneering new theories and building Austra ....New frontiers for nonequilibrium systems. The universe is comprised of systems in states of change or responding to a driving force. Yet a fundamental understanding of these nonequilibrium systems that enables predictive design has eluded scientists to date. This program aims to develop ground-breaking principles and methodologies to predict properties of nonequilibrium systems using both statistical physics and molecular simulations. Significantly, by pioneering new theories and building Australian capacity in this area, we will be able to understand, control and utilise their distinctive behaviour in design. Expected outcomes and benefits are multi-dimensional, including breakthrough theory and new capability for high-end technologies such as nanofluidics, robotics and batteries.Read moreRead less
Scalable and Robust Bayesian Inference for Implicit Statistical Models. This project aims to develop the next generation of efficient methods for fitting complex simulation-based statistical models to data. Practitioners and scientists are interested in such implicit models to enable discoveries, produce accurate predictions and inform decisions under uncertainty. However, the associated computational cost has restricted researchers to implicit models that must have a small number of parameters ....Scalable and Robust Bayesian Inference for Implicit Statistical Models. This project aims to develop the next generation of efficient methods for fitting complex simulation-based statistical models to data. Practitioners and scientists are interested in such implicit models to enable discoveries, produce accurate predictions and inform decisions under uncertainty. However, the associated computational cost has restricted researchers to implicit models that must have a small number of parameters and be well specified, impeding scientific progress. This project will develop new computational methods and algorithms for implicit models that scale to high dimensions and are robust to misspecification. Benefits will arise from the more routine use of implicit models in epidemiology, biology, ecology and other fields.Read moreRead less
Advances in Sequential Monte Carlo Methods for Complex Bayesian Models. This project aims to develop efficient statistical algorithms for parameter estimation of complex stochastic models that currently cannot be handled. Parameter estimation is an essential component of mathematical modelling for answering scientific questions and revealing new insights. Current parameter estimation methods can be inefficient and require too much user intervention. This project will develop novel Bayesian alg ....Advances in Sequential Monte Carlo Methods for Complex Bayesian Models. This project aims to develop efficient statistical algorithms for parameter estimation of complex stochastic models that currently cannot be handled. Parameter estimation is an essential component of mathematical modelling for answering scientific questions and revealing new insights. Current parameter estimation methods can be inefficient and require too much user intervention. This project will develop novel Bayesian algorithms that are optimally automated and efficient by exploiting ever-improving parallel computing devices. The new methods will allow practitioners to process realistic models, enabling new scientific discoveries in a wide range of disciplines such as biology, ecology, agriculture, hydrology and finance.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
A Novel Approach to Semi-Supervised Statistical Machine Learning. Recent successes in the construction of classifiers for making diagnoses and predictions are due in part to their using much data labelled with respect to their class of origin. But typically there are little labelled data but plentiful unlabelled data. The goal of semi-supervised learning (SSL) is to leverage large amounts of unlabelled data to improve the performance using only small labelled datasets and so SSL is of paramount ....A Novel Approach to Semi-Supervised Statistical Machine Learning. Recent successes in the construction of classifiers for making diagnoses and predictions are due in part to their using much data labelled with respect to their class of origin. But typically there are little labelled data but plentiful unlabelled data. The goal of semi-supervised learning (SSL) is to leverage large amounts of unlabelled data to improve the performance using only small labelled datasets and so SSL is of paramount importance to applications where it is expensive or impractical to obtain much labelled data. The project is to develop a novel SSL approach that adopts a missingness mechanism for the missing labels to build a classifier that not only improves accuracy but it can be greater than if the missing labels were known.
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Early Career Industry Fellowships - Grant ID: IE230100263
Funder
Australian Research Council
Funding Amount
$477,037.00
Summary
Improve genomic testing tools for fertility traits in beef cattle. Fertility is a key driver of productivity and profitability for beef industry; however, a substantial industry challenge is poor fertility and the difficulty and expense of measuring fertility in remote Australia. By integrating multiple omics datasets and fifty thousand fertility phenotypes recorded on beef cattle, the project will identify sequence variation, including structural variants, that underpin genetic variation in cat ....Improve genomic testing tools for fertility traits in beef cattle. Fertility is a key driver of productivity and profitability for beef industry; however, a substantial industry challenge is poor fertility and the difficulty and expense of measuring fertility in remote Australia. By integrating multiple omics datasets and fifty thousand fertility phenotypes recorded on beef cattle, the project will identify sequence variation, including structural variants, that underpin genetic variation in cattle fertility. Our industry partner, which genotypes hundreds of thousands of cattle a year, will produce new genotype arrays and novel low-cost sequencing approaches including these variants, enabling selection that could potentially increase herd reproductive rate by 4%, returning $40M per annum to the farmers.Read moreRead less
Large dynamic time-varying models for structural macroeconomic inference. This project aims to broaden the range of macroeconomic models that have an integrated capacity for both greater realism and efficiency in analysis. This approach will be applied to two contexts at the forefront of current macroeconomic research, the effects of noisy productivity signals on business cycles and the effects of fiscal policy shocks. Flexible macro-econometric models underpin accurate inference by economists ....Large dynamic time-varying models for structural macroeconomic inference. This project aims to broaden the range of macroeconomic models that have an integrated capacity for both greater realism and efficiency in analysis. This approach will be applied to two contexts at the forefront of current macroeconomic research, the effects of noisy productivity signals on business cycles and the effects of fiscal policy shocks. Flexible macro-econometric models underpin accurate inference by economists and policymakers and the project outputs should provide widespread and significant benefits by improving policy and boosting Australia’s comparative advantage.Read moreRead less
Improving productivity: theory and application to Australian hospitals. This project aims to improve existing methods for analysing productivity and efficiency of organisations. The new methods will be applied to Australian hospitals, to analyse their productivity and efficiency, identify the best-practices and their determinants and recommend improvements and necessary reforms. The high level of healthcare costs in Australia, about 5 percent of gross domestic product, as well as their rapid and ....Improving productivity: theory and application to Australian hospitals. This project aims to improve existing methods for analysing productivity and efficiency of organisations. The new methods will be applied to Australian hospitals, to analyse their productivity and efficiency, identify the best-practices and their determinants and recommend improvements and necessary reforms. The high level of healthcare costs in Australia, about 5 percent of gross domestic product, as well as their rapid and accelerating growth, imply that application of methods developed through this project may save billions of dollars and, more importantly, thousands of lives. An expected outcome of this project will be superior theoretical and practical methods for analysing productivity and efficiency of economic systems, to enhance understanding of the potential for improvements and of the necessary reforms.Read moreRead less