Deep ocean thermodynamics and climate change. This project aims to obtain new insights into the thermodynamic and transport properties of mixtures containing water, particularly at high pressures, that impact directly on our understanding of climate change processes. The project will involve the use of a polarisable potential for water which has recently been demonstrated to yield predictions of high accuracy. It will be used to model saline water mixtures containing carbon dioxide, resulting in ....Deep ocean thermodynamics and climate change. This project aims to obtain new insights into the thermodynamic and transport properties of mixtures containing water, particularly at high pressures, that impact directly on our understanding of climate change processes. The project will involve the use of a polarisable potential for water which has recently been demonstrated to yield predictions of high accuracy. It will be used to model saline water mixtures containing carbon dioxide, resulting in valuable data for thermodynamic properties of the world's oceans. These data are of crucial importance for accurate climate change predictions and as such the project will have an important impact on understanding our changing environment.Read moreRead less
Uncertainty, Risk and Related Concepts in Machine Learning. Machine learning is the science of making sense of data. It does not and cannot remove all risk and uncertainty. This project proposes to study the foundations of how machine learning uses, represents and communicates risk and uncertainty. It aims to do so by finding new theoretical connections between diverse notions that have arisen in allied disciplines. These include risk, uncertainty, scoring rules and loss functions, divergences, ....Uncertainty, Risk and Related Concepts in Machine Learning. Machine learning is the science of making sense of data. It does not and cannot remove all risk and uncertainty. This project proposes to study the foundations of how machine learning uses, represents and communicates risk and uncertainty. It aims to do so by finding new theoretical connections between diverse notions that have arisen in allied disciplines. These include risk, uncertainty, scoring rules and loss functions, divergences, statistics and different ways of aggregating information. By building a more complete theoretical map it is expected that new machine learning methods will be developed, but more importantly that machine learning will be able to be better integrated into larger socio-technical systems.Read moreRead less
Principled statistical methods for high-dimensional correlation networks. This project aims to develop a novel and principled approach for building correlation networks. Correlation networks aim to identify the most significant associations present in modern massive datasets, and have numerous applications, ranging from the biomedical and environmental sciences to the social sciences. Nodes of such networks represent features, and edges represent associations, or the lack thereof. Current method ....Principled statistical methods for high-dimensional correlation networks. This project aims to develop a novel and principled approach for building correlation networks. Correlation networks aim to identify the most significant associations present in modern massive datasets, and have numerous applications, ranging from the biomedical and environmental sciences to the social sciences. Nodes of such networks represent features, and edges represent associations, or the lack thereof. Current methods are not readily scalable to modern ultra-high dimensional settings, and do not account for uncertainty in the estimated associations. This project will develop a principled, highly scalable methodology for building such networks, which incorporates uncertainty quantification. Emphasis is placed on modern ultra-high dimensional settings in which differentiating a true correlation from a spurious one is a notoriously difficult task.Read moreRead less
Inference for Hawkes processes with challenging data. The Hawkes processes are statistical models for the analysis of high-impact event sequences, such as bushfires, earthquakes, infectious diseases, and cyber attacks. When the times and/or marks are missing for some events or when the data is otherwise incomplete, it is challenging to fit these models and perform diagnostic checks on the fitted models. This project aims to develop novel statistical methods to fit these models in the presence of ....Inference for Hawkes processes with challenging data. The Hawkes processes are statistical models for the analysis of high-impact event sequences, such as bushfires, earthquakes, infectious diseases, and cyber attacks. When the times and/or marks are missing for some events or when the data is otherwise incomplete, it is challenging to fit these models and perform diagnostic checks on the fitted models. This project aims to develop novel statistical methods to fit these models in the presence of incomplete data and to check the goodness-of-fit of the fitted models. The expected outcomes include publications documenting these methods and software packages implementing them. The primary benefits include the advancement of statistical methodology and the training of junior research personnel. Read moreRead less
New insights into abrupt climate change using isotope model-data fusion. There have been several episodes in the past 60,000 years during which carbon dioxide was released into the atmosphere causing significant and rapid warming. This project aims to determine the source of the carbon responsible for these warming events. This project is significant because it will increase our understanding of the processes underlying abrupt past, and potential future changes. These processes are currently und ....New insights into abrupt climate change using isotope model-data fusion. There have been several episodes in the past 60,000 years during which carbon dioxide was released into the atmosphere causing significant and rapid warming. This project aims to determine the source of the carbon responsible for these warming events. This project is significant because it will increase our understanding of the processes underlying abrupt past, and potential future changes. These processes are currently underestimated or missing in climate models. The expected outcomes include a better understanding of the non-linear responses in the climate system. This should provide significant benefits, such as an assessment of whether the smooth climate changes currently projected through to year 2100 are misleading.Read moreRead less
Fast flexible feature selection for high dimensional challenging data. The project aims to provide new frameworks for fast flexible feature selection and appropriate modelling of heterogeneous data through structural varying-coefficient regression models. The outcomes will be a series of new statistical methods and concepts enabling more powerful modelling of complex bioscience data. The project will create the science for building reliable statistical models taking model uncertainty into accoun ....Fast flexible feature selection for high dimensional challenging data. The project aims to provide new frameworks for fast flexible feature selection and appropriate modelling of heterogeneous data through structural varying-coefficient regression models. The outcomes will be a series of new statistical methods and concepts enabling more powerful modelling of complex bioscience data. The project will create the science for building reliable statistical models taking model uncertainty into account, impacting how results will be interpreted, and with accompanying software. This will be a significant improvement in the assessment of model confidence in the food and health research priority areas including areas such as meat science, Huntington’s disease, and kidney transplantation.Read moreRead less
Fast approximate inference methods: new algorithms, applications and theory. This project aims to develop new algorithms and theory for fast approximate inference and lay down infrastructure to aid future extensions. Fast approximate inference methods are a principled and extensible means of fitting large and complex statistical models to big data sets. They come into their own in applications where speed is paramount and traditional approaches are not feasible. The project aims to lead to prac ....Fast approximate inference methods: new algorithms, applications and theory. This project aims to develop new algorithms and theory for fast approximate inference and lay down infrastructure to aid future extensions. Fast approximate inference methods are a principled and extensible means of fitting large and complex statistical models to big data sets. They come into their own in applications where speed is paramount and traditional approaches are not feasible. The project aims to lead to practical outcomes from better business decision-making for insurance data warehouses, to improved medical imaging technology.Read moreRead less
Learning the meso-scale organization of complex networks. This project aims to model and learn the organization of online social networks. We will combine mathematical models, inference, and domain knowledge from computational social sciences to obtain interpretable descriptions of the role groups of users play in the network. The expected outcomes are new mathematical models and computational methods that learn from data how to best decompose a complex network into building blocks and their int ....Learning the meso-scale organization of complex networks. This project aims to model and learn the organization of online social networks. We will combine mathematical models, inference, and domain knowledge from computational social sciences to obtain interpretable descriptions of the role groups of users play in the network. The expected outcomes are new mathematical models and computational methods that learn from data how to best decompose a complex network into building blocks and their interactions, linking connectivity to function. This should provide benefits to industries and policy makers interested in how information spreads in social media, including the critical questions of understanding the mechanisms contributing to political polarization and fragmentation.Read moreRead less
Local climate changes caused by large bushfire burnt areas. This project aims to quantify the impact on local climate produced by large burnt areas after extreme bushfires. This project expects to generate new knowledge on these previously unexplored fire-scar induced changes to local climate. It will extend an innovative approach that combines satellite based earth observation with very high resolution regional climate modelling to quantify the impacts on land-atmosphere feedbacks and local cli ....Local climate changes caused by large bushfire burnt areas. This project aims to quantify the impact on local climate produced by large burnt areas after extreme bushfires. This project expects to generate new knowledge on these previously unexplored fire-scar induced changes to local climate. It will extend an innovative approach that combines satellite based earth observation with very high resolution regional climate modelling to quantify the impacts on land-atmosphere feedbacks and local climate. Expected outcomes of this project include enhanced methods to quantify local climate changes after extreme fires and their effect on vegetation recovery. This should provide significant benefits to the planning for, and management of, vegetation recovery after extreme fires.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