Discovery Early Career Researcher Award - Grant ID: DE200100200
Funder
Australian Research Council
Funding Amount
$418,398.00
Summary
Next generation causal inference methods for biological data. This project aims to develop next generation causal inference methods for analysing biological data especially the single cell sequencing data and their applications in cell biology. Although Artificial Intelligence and Statistical Machine Learning have been applied successfully in many fields, including biological research, there is still a serious lack of methods for interpreting and reasoning about the mechanism of biological syste ....Next generation causal inference methods for biological data. This project aims to develop next generation causal inference methods for analysing biological data especially the single cell sequencing data and their applications in cell biology. Although Artificial Intelligence and Statistical Machine Learning have been applied successfully in many fields, including biological research, there is still a serious lack of methods for interpreting and reasoning about the mechanism of biological systems, the ultimate goal of research in many areas. Efficient data-driven causality discovery approaches developed by the project will be a timely and significant contribution to the knowledge of biology and statistics as well as the battle against health threats.
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Prediction of inertial particle focusing in curved microfluidic ducts. This project aims to develop mathematical models to predict migration of particles suspended in flow through curved microfluidic ducts and their focusing by size to different regions in the cross-section of the duct. New knowledge in mathematics and engineering will be generated through models that capture the two-way force balance between fluid and particles and by a novel use of asymptotics for computational efficiency. Exp ....Prediction of inertial particle focusing in curved microfluidic ducts. This project aims to develop mathematical models to predict migration of particles suspended in flow through curved microfluidic ducts and their focusing by size to different regions in the cross-section of the duct. New knowledge in mathematics and engineering will be generated through models that capture the two-way force balance between fluid and particles and by a novel use of asymptotics for computational efficiency. Expected outcomes are understanding of the physics that drives particle migration and the parameters that may be used to control particle focusing. This will benefit design and operation of microfluidic devices for particle sorting as required for "liquid biopsy", the isolation of cancer cells in a routine blood sample.Read moreRead less
Mathematical modelling of information flow in social networks. This proposal aims to develop new mathematical and statistical methods to understand information flow in social networks. By using novel information theoretic techniques, it will create new methods to characterise social information flow in social networks. These tools will allow derivation of fundamental limits of predictability for AI methods applied to digital data. New mathematics of information flow will produce insights into so ....Mathematical modelling of information flow in social networks. This proposal aims to develop new mathematical and statistical methods to understand information flow in social networks. By using novel information theoretic techniques, it will create new methods to characterise social information flow in social networks. These tools will allow derivation of fundamental limits of predictability for AI methods applied to digital data. New mathematics of information flow will produce insights into social influence in online social networks. Benefits include: better understanding of how echo chambers may form in social networks, predictive models for how misinformation can spread online such as during an emergency, and a framework for intercomparison of AI methods applied to digital data on individuals. Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE240100097
Funder
Australian Research Council
Funding Amount
$389,670.00
Summary
Mathematical models for actin scavenging and biofilm removal. The project aims to develop mathematical models for actin scavenging and biofilm removal, processes that combine to alleviate tissue damage and inflammation. Actin scavenging eliminates the protein F-actin which is released during cell death, but this process is not fully-understood. Biofilms are colonies of micro-organisms, for example bacteria, that are highly resistant to antimicrobial treatment. This project expects to generate ne ....Mathematical models for actin scavenging and biofilm removal. The project aims to develop mathematical models for actin scavenging and biofilm removal, processes that combine to alleviate tissue damage and inflammation. Actin scavenging eliminates the protein F-actin which is released during cell death, but this process is not fully-understood. Biofilms are colonies of micro-organisms, for example bacteria, that are highly resistant to antimicrobial treatment. This project expects to generate new knowledge, using an innovative combination of mathematical modelling and cell biology experiments. Expected outcomes include new theory and software, yielding the benefits of increased understanding of cell biology, and potential to enhance development of smart materials that eliminate biofilms.Read moreRead less
Price-Setting Rules and Allocative Efficiency in Oligopolies. This project aims to investigate under which circumstances restrictions on how often firms can change prices increase competition in an oligopoly and bring down prices. For this, we propose the use of laboratory experiments with a novel design followed by field experiments and a real price-data analysis for external validation. This study will result in both the advancement of theory describing how firms compete in dynamic oligopolies ....Price-Setting Rules and Allocative Efficiency in Oligopolies. This project aims to investigate under which circumstances restrictions on how often firms can change prices increase competition in an oligopoly and bring down prices. For this, we propose the use of laboratory experiments with a novel design followed by field experiments and a real price-data analysis for external validation. This study will result in both the advancement of theory describing how firms compete in dynamic oligopolies and practical policy advice on how price setting rules can be used to improve consumer welfare. This project has the potential to generate sizable benefits to Australian consumers, as the resulting policy advice would be applicable to large markets such as those for petrol, groceries and online retail.Read moreRead less
Immersive analytics: interactive data analysis using surfaces and spaces. This project aims to explore the potential for new immersive display and interaction technologies to greatly enhance the field of visual data analytics. Humans struggle to understand the masses of complex data they now accumulate. Visual data analytics offers a solution. The project expects to provide practical and theoretical frameworks for immersive data analysis and valuable intellectual property on the first practical ....Immersive analytics: interactive data analysis using surfaces and spaces. This project aims to explore the potential for new immersive display and interaction technologies to greatly enhance the field of visual data analytics. Humans struggle to understand the masses of complex data they now accumulate. Visual data analytics offers a solution. The project expects to provide practical and theoretical frameworks for immersive data analysis and valuable intellectual property on the first practical tools for immersive data analytics. This will provide significant benefits, such as allowing those across government and industry to make more informed decisions from data.Read moreRead less
Mathematics to underpin and drive novel inertial microfluidic technologies. Particles suspended in flow through microfluidic ducts migrate under inertial and drag forcing to different regions in the cross-section depending on particle size, duct geometry and control parameters, enabling isolation of, for example, cancer cells/microplastics from a blood/water sample. Device design needs mathematical models yielding understanding of the particle dynamics, and tools for determining geometry and con ....Mathematics to underpin and drive novel inertial microfluidic technologies. Particles suspended in flow through microfluidic ducts migrate under inertial and drag forcing to different regions in the cross-section depending on particle size, duct geometry and control parameters, enabling isolation of, for example, cancer cells/microplastics from a blood/water sample. Device design needs mathematical models yielding understanding of the particle dynamics, and tools for determining geometry and control parameters. Particle boundary conditions strongly influence the inertial lift and drag forces that drive particle motion. This project will develop these mathematical tools for boundary conditions applicable to both passive and active particles, so driving development of novel devices for existing and new applications.Read moreRead less
New Paradigms for Robust Fitting: Kernelisation and Polyhedral Search. Outliers inevitably exist in visual data due to imperfect data acquisition or preprocessing. To enable computer vision applications that can perform reliably, robust fitting algorithms are necessary to counter the biasing influence of outliers. However, current robust algorithms are unsatisfactory: they are unreliable (due to using randomisation) or too computationally costly (due to using exhaustive search). This project wil ....New Paradigms for Robust Fitting: Kernelisation and Polyhedral Search. Outliers inevitably exist in visual data due to imperfect data acquisition or preprocessing. To enable computer vision applications that can perform reliably, robust fitting algorithms are necessary to counter the biasing influence of outliers. However, current robust algorithms are unsatisfactory: they are unreliable (due to using randomisation) or too computationally costly (due to using exhaustive search). This project will develop new robust algorithms to mitigate these shortcomings. It will do so by investigating two new paradigms of kernelisation and polyhedral search, which offer unprecedented theoretical insights into the problem. The outcomes will contribute towards computer vision applications that are more practical and reliable.Read moreRead less
Understanding the mechanisms that inhibit and promote biofilm expansion. Yeasts have been used for biotechnology throughout recorded history. They are important human pathogens, and major experimental models of eukaryotic cells. Although yeasts are some of the most studied organisms in biology, their modes of colony biofilm formation are not fully understood. Methods to investigate the environmental and genetic processes that drive colony biofilm formation will be developed in this proposed pro .... Understanding the mechanisms that inhibit and promote biofilm expansion. Yeasts have been used for biotechnology throughout recorded history. They are important human pathogens, and major experimental models of eukaryotic cells. Although yeasts are some of the most studied organisms in biology, their modes of colony biofilm formation are not fully understood. Methods to investigate the environmental and genetic processes that drive colony biofilm formation will be developed in this proposed project. They will provide a deeper understanding of the mechanisms that inhibit and promote biofilm formation, and colonial morphology in the different modes of growth of Saccharomyces cerevisiae, with implications for this and other biofilm-forming yeasts of biotechnological or medical importance.Read moreRead less