Towards Generalisable and Unbiased Dynamic Recommender Systems. This project aims to develop the foundations, including models, methodology, and algorithms for building generalisable and unbiased dynamic recommender systems to facilitate intelligent decision-making, prompt contextualised and personalised strategic plans, and support context-aware action recourse. To ensure that fundamental principles, such as fairness and transparency, are respected, a set of algorithms and techniques are propos ....Towards Generalisable and Unbiased Dynamic Recommender Systems. This project aims to develop the foundations, including models, methodology, and algorithms for building generalisable and unbiased dynamic recommender systems to facilitate intelligent decision-making, prompt contextualised and personalised strategic plans, and support context-aware action recourse. To ensure that fundamental principles, such as fairness and transparency, are respected, a set of algorithms and techniques are proposed to develop recommender systems in a more responsible manner. The result of this project will not only maintain Australia's leadership in this frontier research area, but also serve as an excellent vehicle for the education and training of Australia's next generation of scholars and engineers.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
Understanding Dynamic Aspects of Economic Inequality. This project aims to study dynamic aspects of inequality in Australia by exploring the changes in labour and housing market conditions and their relation to the changes in the distribution of income and wealth over the last decade. The project also aims to develop new econometric techniques to examine the factors that are responsible for the changes in the distribution of income and wealth and a range of labour and housing market outcomes. Pa ....Understanding Dynamic Aspects of Economic Inequality. This project aims to study dynamic aspects of inequality in Australia by exploring the changes in labour and housing market conditions and their relation to the changes in the distribution of income and wealth over the last decade. The project also aims to develop new econometric techniques to examine the factors that are responsible for the changes in the distribution of income and wealth and a range of labour and housing market outcomes. Particular attention will be paid to the role of the changes in individual-specific characteristics (such as education, age, employment status, and occupation) and neighbourhood-specific characteristics (such as house prices and population ageing) in producing inequality.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
Rigorous Privacy Compliance in Modern Application Ecosystems. Modern network applications such as mobile applications and browser extensions have become the primary gateways for consumers to access the Internet in today’s digital landscape. This project aims to address privacy issues in these ecosystems by developing a new privacy-compliance assessment framework. The framework will evaluate the current privacy practices of application ecosystems, enabling users and developers in Australia and wo ....Rigorous Privacy Compliance in Modern Application Ecosystems. Modern network applications such as mobile applications and browser extensions have become the primary gateways for consumers to access the Internet in today’s digital landscape. This project aims to address privacy issues in these ecosystems by developing a new privacy-compliance assessment framework. The framework will evaluate the current privacy practices of application ecosystems, enabling users and developers in Australia and worldwide to reliably identify potential privacy risks and issues on their applications. The intended outcomes should endow data controllers with the capability of evidencing their compliance of data protection legislations such as Australia Privacy Act 1988 and EU General Data Protection Regulation (GDPR).Read moreRead less
Theoretical Foundations of Ethical Machine Learning. The project aims to develop a systematic theory of ethical machine learning. Machine learning is a powerful and pervasive technology that is already having a huge impact on Australia. When applied to data about people there are a range of ethical harms that can arise (fairness, and privacy are two of them). The project aims to develop a rigorously grounded foundation for managing such ethical harms. For example it will allow the quantification ....Theoretical Foundations of Ethical Machine Learning. The project aims to develop a systematic theory of ethical machine learning. Machine learning is a powerful and pervasive technology that is already having a huge impact on Australia. When applied to data about people there are a range of ethical harms that can arise (fairness, and privacy are two of them). The project aims to develop a rigorously grounded foundation for managing such ethical harms. For example it will allow the quantification of the inevitable trade-offs between fairness and utility. The benefits of the project should include better ways of managing these trade-offs, a competitive advantage for Australian firms developing the technology, and will ensure that the country retains a social license to use the technology.Read moreRead less
Complex data, model selection and bootstrap inference. The project will provide new statistical methods and associated software for the analysis and modelling of complex data, as well as quality research training. This project will benefit researchers in statistics and users of statistics who encounter the complex data considered in this project and who need to model and make inferences from these data. Since these kinds of data arise in many areas (such as medicine, genetics, chemistry etc), ....Complex data, model selection and bootstrap inference. The project will provide new statistical methods and associated software for the analysis and modelling of complex data, as well as quality research training. This project will benefit researchers in statistics and users of statistics who encounter the complex data considered in this project and who need to model and make inferences from these data. Since these kinds of data arise in many areas (such as medicine, genetics, chemistry etc), Australia and Australian industry will ultimately benefit from the proposed research. The strengthening of international link and the training of highly trained research scientists in an area of national importance will also benefit Australia.Read moreRead less
Bootstrap methods for data with multiple errors. This project will provide new methods for data analysis and quality research training. The results will benefit researchers in statistics and users of statistics who encounter data with multiple errors and who need to make inferences from these data. The many areas from which such data arise (including medicine, genetics, chemistry, education, social surveys etc) mean that Australia and Australian Industry will also ultimately benefit from the r ....Bootstrap methods for data with multiple errors. This project will provide new methods for data analysis and quality research training. The results will benefit researchers in statistics and users of statistics who encounter data with multiple errors and who need to make inferences from these data. The many areas from which such data arise (including medicine, genetics, chemistry, education, social surveys etc) mean that Australia and Australian Industry will also ultimately benefit from the research. The strengthening of international links and the training of highly trained researchers will also benefit the Australian community.Read moreRead less
Experimental Demonstrations of New Theorems of Nonequilibrium Thermodynamics. In the last decade, two theorems have been proposed to revolutionise the field of thermodynamics. These theorems lift the restriction of the thermodynamic limit, allowing thermodynamic concepts to be applied to small systems such as nanomachines, and characterise systems that may be far-from-equilibrium. These theorems are at odds with a traditional understanding of 19th century thermodynamics where equilibrium is cent ....Experimental Demonstrations of New Theorems of Nonequilibrium Thermodynamics. In the last decade, two theorems have been proposed to revolutionise the field of thermodynamics. These theorems lift the restriction of the thermodynamic limit, allowing thermodynamic concepts to be applied to small systems such as nanomachines, and characterise systems that may be far-from-equilibrium. These theorems are at odds with a traditional understanding of 19th century thermodynamics where equilibrium is central and the Second Law inviolate. However they are critical to the application of thermodynamic concepts to modern systems of the 21st century. Using Optical Tweezers, we will experimentally demonstrate these theorems, present irrefutable evidence of their validity, and demonstrate their application in modern systems.Read moreRead less
Experimental Demonstrations of Violations of the Second Law of Thermodynamics. Inventors and engineers strive to scale-down machines, devices and engines to nanometre sizes for a range of technological purposes and scientists investigate protein motors to understand their operation in hopes of modifying their biological behaviour. However, according to a new theorem in Non-equilibrium Statistical Mechanics, there is a fundamental limit to this scaling-down of engines: such nanomachines, includi ....Experimental Demonstrations of Violations of the Second Law of Thermodynamics. Inventors and engineers strive to scale-down machines, devices and engines to nanometre sizes for a range of technological purposes and scientists investigate protein motors to understand their operation in hopes of modifying their biological behaviour. However, according to a new theorem in Non-equilibrium Statistical Mechanics, there is a fundamental limit to this scaling-down of engines: such nanomachines, including protein motors, will run in "reverse" for appreciable amounts of time and in violation of the Second Law of Thermodynamics. We propose to demonstrate this inescapable, operational limit in nanotechnology with experiments using an Optical Tweezers apparatus.Read moreRead less