Harmonic analysis of Laplacians in curved spaces. Harmonic Analysis is a branch of mathematics which is interrelated to other fields of mathematics like complex analysis, number theory and partial differential equations (pdes) with many applications in engineering and technology. This project aims to solve a number of difficult fundamental problems at the frontier of harmonic analysis in understanding Laplacians in curved spaces. Such Laplacians control the propagation of heat and waves on manif ....Harmonic analysis of Laplacians in curved spaces. Harmonic Analysis is a branch of mathematics which is interrelated to other fields of mathematics like complex analysis, number theory and partial differential equations (pdes) with many applications in engineering and technology. This project aims to solve a number of difficult fundamental problems at the frontier of harmonic analysis in understanding Laplacians in curved spaces. Such Laplacians control the propagation of heat and waves on manifolds and Lie groups, arising in mathematical physics and quantum mechanics. Expected outcomes are the solutions of dispersive equations and the framework of singular integrals in curved spaces; new ideas and techniques in harmonic analysis developed; and training of Australian future mathematicians.Read moreRead less
High-resolution multiscale modelling of pandemics: COVID-19 and beyond. The project aims to develop high-resolution computational models for pandemic mitigation and control, focussing on the novel coronavirus and its emerging variants, and leveraging demographic, genomic and epidemiological data. It expects to rigorously compare multi-scale effects of complex vaccination and social distancing strategies and quantify optimal responses under the COVID-19 induced uncertainty. The intended outcomes ....High-resolution multiscale modelling of pandemics: COVID-19 and beyond. The project aims to develop high-resolution computational models for pandemic mitigation and control, focussing on the novel coronavirus and its emerging variants, and leveraging demographic, genomic and epidemiological data. It expects to rigorously compare multi-scale effects of complex vaccination and social distancing strategies and quantify optimal responses under the COVID-19 induced uncertainty. The intended outcomes include computational models of how the most infectious viral variants emerge and spread in presence of interventions, how to predict the outbreaks, and which are the most vulnerable communities. This should make a significant economic and social impact, improving population health while maintaining a resilient economy.Read moreRead less
Quantifying emergence and dynamics of foodborne epidemics in Australia. The project aims to greatly improve the accuracy and scope of computational epidemiological models predicting emergence and evolution of foodborne diseases in Australia. It expects to reveal key pathways for both biological evolution of microorganisms, and their spread though food supply chains and human interactions. The intended outcomes include discovering how the most dominant strains of foodborne infection emerge and se ....Quantifying emergence and dynamics of foodborne epidemics in Australia. The project aims to greatly improve the accuracy and scope of computational epidemiological models predicting emergence and evolution of foodborne diseases in Australia. It expects to reveal key pathways for both biological evolution of microorganisms, and their spread though food supply chains and human interactions. The intended outcomes include discovering how the most dominant strains of foodborne infection emerge and self-organise in complex networks, how to predict and contain the epidemics closer to their source, and which are the most vulnerable groups and communities. This should make a significant economic and social impact, improving health of the population, while also safeguarding national and international supply chains.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
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
Intelligent pattern recognition of water end uses enabling recommendations. This project aims to develop a hybrid machine learning method for autonomously disaggregating high- and low-resolution water flow data received from smart meters into discrete end-use events, and a customised recommender system for efficient resource demand management. Project novelty and significance relates to this coupling and autonomous disaggregation of datasets from advanced sensors, enabling more efficient utility ....Intelligent pattern recognition of water end uses enabling recommendations. This project aims to develop a hybrid machine learning method for autonomously disaggregating high- and low-resolution water flow data received from smart meters into discrete end-use events, and a customised recommender system for efficient resource demand management. Project novelty and significance relates to this coupling and autonomous disaggregation of datasets from advanced sensors, enabling more efficient utility services delivery and lower customer utility bills. Project benefits include enabling utilities to better manage and plan resources in the information age, while empowering customers with real-time water end-use data and behaviour changing consumption recommendations.Read moreRead less
Congestion control in complex networks with higher-order interactions. Traffic congestion significantly costs the Australian economy and environment. This project aims to develop ground-breaking network models of urban traffic systems to build a new congestion control framework. The purpose of network modelling is to capture the interdependence between different parts of traffic systems, which facilitates studying congestion cascade within the network. The project expects to generate next genera ....Congestion control in complex networks with higher-order interactions. Traffic congestion significantly costs the Australian economy and environment. This project aims to develop ground-breaking network models of urban traffic systems to build a new congestion control framework. The purpose of network modelling is to capture the interdependence between different parts of traffic systems, which facilitates studying congestion cascade within the network. The project expects to generate next generation of network models for more effective congestion control. Expected outcomes include novel congestion control technologies that adjust traffic signals in real-time to optimally utilise the available road space. This should provide significant economic and environmental benefits to Australians by easing traffic jams.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE140101375
Funder
Australian Research Council
Funding Amount
$395,220.00
Summary
The forest and the trees: How global brain rhythms facilitate local information processing. One of the greatest challenges in understanding the brain is the enormous range of scales it operates on, from single neurons a few microns across to entire hemispheres on the scale of tens of centimetres. This project will investigate how large-scale brain rhythms influence and facilitate information processing, particularly motor control, among small networks of individual neurons. The research question ....The forest and the trees: How global brain rhythms facilitate local information processing. One of the greatest challenges in understanding the brain is the enormous range of scales it operates on, from single neurons a few microns across to entire hemispheres on the scale of tens of centimetres. This project will investigate how large-scale brain rhythms influence and facilitate information processing, particularly motor control, among small networks of individual neurons. The research questions will be addressed by combining detailed computer simulations with data-driven analyses of empirical human and monkey brain dynamics. The outcomes of this project will provide a richer understanding of how our brains encode and process information, leading to practical benefits such as improved control of artificial limbs.Read moreRead less
The philosophical influences on anthropology. This project aims to undertake a comprehensive account of Kant’s impact on the early history of anthropology, offering a new framework for understanding philosophy’s role as a cultural force in society. The project will investigate the importance of Kant’s twin narratives of progressive human development and racial difference for understanding the course taken by anthropology when determining government policies regarding race relations. The benefit ....The philosophical influences on anthropology. This project aims to undertake a comprehensive account of Kant’s impact on the early history of anthropology, offering a new framework for understanding philosophy’s role as a cultural force in society. The project will investigate the importance of Kant’s twin narratives of progressive human development and racial difference for understanding the course taken by anthropology when determining government policies regarding race relations. The benefit of this reconstruction will be the identification of contemporary examples of Kant’s continued legacy, especially in the context of legacies of racial bias, and to the nature of claimed racial and ethnic identities.Read moreRead less
The experimental self in early modern philosophy. This project aims to analyse an unrecognised connection between the emergence of experience-grounded theories of the self and the rise of early modern experimentalism. These are two developments in early modern philosophy which paved the way for today’s conception of science. By studying the influence of Cartesian physiology on early modern accounts of embodied cognition, the project seeks to correct some of the misconceptions of the period. The ....The experimental self in early modern philosophy. This project aims to analyse an unrecognised connection between the emergence of experience-grounded theories of the self and the rise of early modern experimentalism. These are two developments in early modern philosophy which paved the way for today’s conception of science. By studying the influence of Cartesian physiology on early modern accounts of embodied cognition, the project seeks to correct some of the misconceptions of the period. The project aims to clarify the role of human agency in advancement of knowledge, and develop a conceptual framework that allows a more nuanced and complex understanding of the relationship between the humanities and sciences.Read moreRead less