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
Macroeconomic and Financial Modelling in an Era of Extremes. This project aims to develop methods to allow workhorse models in economics and finance to better reflect tail events--low probability extreme events, such as the Global Financial Crisis and the COVID-19 pandemic. It intends to address fundamental technical challenges in the estimation of such models, develop a coherent framework for counterfactual analysis of these models and propose methods to apply these models in a big-data environ ....Macroeconomic and Financial Modelling in an Era of Extremes. This project aims to develop methods to allow workhorse models in economics and finance to better reflect tail events--low probability extreme events, such as the Global Financial Crisis and the COVID-19 pandemic. It intends to address fundamental technical challenges in the estimation of such models, develop a coherent framework for counterfactual analysis of these models and propose methods to apply these models in a big-data environment. Expected outcomes include new insights into the transmission of tail risks in the global economic and financial system. This should provide significant benefits, including guidance to Australian and international policymakers charged with maintaining stability in the face of extreme events.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE210100549
Funder
Australian Research Council
Funding Amount
$417,328.00
Summary
Adaptive evolution and its demographic consequences today. This project aims to provide the first test of whether the rate of adaptative genetic evolution has changed in the recent decades, to quantify how much recent genetic evolution helps animal populations survive, and to increase the ability to study on-going genetic evolution in Australian wildlife. The project is of major significance as many species are currently threatened, or invading, due to rapid environmental changes, in particular ....Adaptive evolution and its demographic consequences today. This project aims to provide the first test of whether the rate of adaptative genetic evolution has changed in the recent decades, to quantify how much recent genetic evolution helps animal populations survive, and to increase the ability to study on-going genetic evolution in Australian wildlife. The project is of major significance as many species are currently threatened, or invading, due to rapid environmental changes, in particular climate change. The anticipated outcome of the project is to deliver new methods, establish a network of international and national collaborators and improve the ability to measure and to forecast how Australian animals adapt to rapidly changing environments.Read moreRead less
Feature Learning for High-dimensional Functional Time Series. This project aims to develop new methods and theories for common features on high-dimensional functional time series observed in empirical applications. The significance includes addressing a key gap in adaptive and efficient feature learning, improving forecasting accuracy and understanding forecasting-driven factors comprehensively for empirical data. Expected outcomes involve advances in big data theory and easy-to-implement algori ....Feature Learning for High-dimensional Functional Time Series. This project aims to develop new methods and theories for common features on high-dimensional functional time series observed in empirical applications. The significance includes addressing a key gap in adaptive and efficient feature learning, improving forecasting accuracy and understanding forecasting-driven factors comprehensively for empirical data. Expected outcomes involve advances in big data theory and easy-to-implement algorithms for applied researchers. This project benefits not only advanced manufacturing by finding optimal stopping time for wood panel compression, but also superior forecasting for mortality in demography, climate data in environmental science, asset returns in finance, and electricity consumption in economics. Read moreRead less
Solving and estimating dynamic models of strategic interaction. This project aims to investigate how firms interact with each other through time and how these interactions drive both the operation of, and value created in, economic markets. While recent theoretical models predominantly capture the complexity of these dynamic interactions, the methods for testing these models’ predictions against observed data do not. Instead, they are based on a range of simplifying assumptions that undermine th ....Solving and estimating dynamic models of strategic interaction. This project aims to investigate how firms interact with each other through time and how these interactions drive both the operation of, and value created in, economic markets. While recent theoretical models predominantly capture the complexity of these dynamic interactions, the methods for testing these models’ predictions against observed data do not. Instead, they are based on a range of simplifying assumptions that undermine the reliability of their analysis. This project will develop statistical and computational methods to better understand observed economic behaviour. By allowing the effects of proposed economic interventions and regulations ex ante, this project will support the development of more efficient and better-targeted policies in every area of the economy.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200100056
Funder
Australian Research Council
Funding Amount
$403,019.00
Summary
Statistical shape analysis using persistent homology. Statistical shape analysis is the quantitative study of variation in geometric shape. An innovative approach applies concepts from algebraic topology in the form of the persistent homology transform. This project aims to prove mathematical theory relating to the persistent homology transform, to develop new statistical theory and methodology, and to apply this theory to a range of applications including the analysis of bird beaks, human skull ....Statistical shape analysis using persistent homology. Statistical shape analysis is the quantitative study of variation in geometric shape. An innovative approach applies concepts from algebraic topology in the form of the persistent homology transform. This project aims to prove mathematical theory relating to the persistent homology transform, to develop new statistical theory and methodology, and to apply this theory to a range of applications including the analysis of bird beaks, human skulls and boundary contours of stem cells. An anticipated goal is the generation of new and significant theoretical results in topological data analysis. Expected outcomes include a topologically motivated platform for shape analysis that is statistically rigorous and has firm mathematical foundations.
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Discovery Early Career Researcher Award - Grant ID: DE210101235
Funder
Australian Research Council
Funding Amount
$424,500.00
Summary
Encounters with hominins: the history of human arrival in Sahul. This project aims to provide a detailed understanding on the remarkably complex encounters between archaic and modern human populations in Island Southeast Asia, New Guinea and Australia during the Pleistocene. The project plans to provide the largest collection of human genetic diversity from this vast geographical region and significantly advance current knowledge on one of the most intriguing questions in human evolution. These ....Encounters with hominins: the history of human arrival in Sahul. This project aims to provide a detailed understanding on the remarkably complex encounters between archaic and modern human populations in Island Southeast Asia, New Guinea and Australia during the Pleistocene. The project plans to provide the largest collection of human genetic diversity from this vast geographical region and significantly advance current knowledge on one of the most intriguing questions in human evolution. These insights are expected to bring important social and cultural benefits for Australia by unveiling the singularly deep genetic history of Aboriginal Australians, including their ancient connection to indigenous communities from Indonesia and New Guinea that extends back to when people first arrived in Australia.
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Transitions between modes of sex-determination in a changing world. Sex-determination controls the largest variation within animals—the division into males and females. While the different systems of sex-determination—involving genetic or environmental control—are fairly well understood, transitions between these systems remain enigmatic in evolutionary biology. This project aims to address this gap by revealing the molecular change required to transition between systems, using one of only two k ....Transitions between modes of sex-determination in a changing world. Sex-determination controls the largest variation within animals—the division into males and females. While the different systems of sex-determination—involving genetic or environmental control—are fairly well understood, transitions between these systems remain enigmatic in evolutionary biology. This project aims to address this gap by revealing the molecular change required to transition between systems, using one of only two known lizard species exhibiting both genetic and temperature control of sex. This knowledge will have important implications for species conservation, facilitating predictions of highly biased sex ratios under climate change, plus potential commercial applications for species where production of one sex is favoured.Read moreRead less
Reliable and accurate statistical solutions for modern complex data. This project aims to develop novel methods for reliable and accurate statistical modelling with modern, complex correlated and error-prone data. The project expects to make significant strides towards future-proofing statistical data analysis, equipping practitioners with a suite of robust and computationally efficient methods which provide confidence in the stability and reproducibility of results obtained, while offering guar ....Reliable and accurate statistical solutions for modern complex data. This project aims to develop novel methods for reliable and accurate statistical modelling with modern, complex correlated and error-prone data. The project expects to make significant strides towards future-proofing statistical data analysis, equipping practitioners with a suite of robust and computationally efficient methods which provide confidence in the stability and reproducibility of results obtained, while offering guarantees on their transferability over a range of populations. This will provide important benefits as they are applied in predicting endangered marine species for fisheries conservation, and in enhancing our national understanding of the relationship between education achievement and financial success. Read moreRead less