Discovery Early Career Researcher Award - Grant ID: DE200101791
Funder
Australian Research Council
Funding Amount
$427,082.00
Summary
Mathematically optimal R&D for coral reef conservation. This project aims to develop mathematical methodologies for optimising Research & Development (R&D) of technologies that will secure complex and uncertain ecosystems into the future. Current conventional management approaches will not prevent the degradation of threatened ecosystems like the Great Barrier Reef, so new technologies are needed. The biggest challenge in choosing these technologies is the long delay between development and depl ....Mathematically optimal R&D for coral reef conservation. This project aims to develop mathematical methodologies for optimising Research & Development (R&D) of technologies that will secure complex and uncertain ecosystems into the future. Current conventional management approaches will not prevent the degradation of threatened ecosystems like the Great Barrier Reef, so new technologies are needed. The biggest challenge in choosing these technologies is the long delay between development and deployment, in which time ecosystem function may collapse and complex, dynamic ecological and social systems will change. The mathematical methods and theory developed will inform a Great Barrier Reef case study, and will be ready for rapid application to other ecosystems as the urgent need arises.Read moreRead less
Mathematical Decision Support to Optimise Hospital Capacity and Utilisation. Hospital planners and executives regularly contend with challenging capacity related decisions. Decisions relating to prioritisation, allocation and sharing of resources have a profound impact on productivity, efficiency and patient outcomes. There is a lack of data-driven or quantitative decision support to make well-informed capacity related decisions of a strategic or tactical nature in a single hospital, or across a ....Mathematical Decision Support to Optimise Hospital Capacity and Utilisation. Hospital planners and executives regularly contend with challenging capacity related decisions. Decisions relating to prioritisation, allocation and sharing of resources have a profound impact on productivity, efficiency and patient outcomes. There is a lack of data-driven or quantitative decision support to make well-informed capacity related decisions of a strategic or tactical nature in a single hospital, or across a regional healthcare system. This project aims to deliver decision support for holistic hospital capacity assessment and planning optimisation. This will yield significant benefits for the health sector, providing a tool to optimise the allocation of resources and provision of infrastructure for regional hospital services.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200100063
Funder
Australian Research Council
Funding Amount
$394,398.00
Summary
Nonmonotone Algorithms in Operator Splitting, Optimisation and Data Science. This project aims to develop the mathematical foundations for the analysis and development of optimisation algorithms used in data science. Despite their now ubiquitous use, machine learning software packages routinely rely on a number of algorithms from mathematical optimisation which are not properly understood. By moving beyond the traditional realms of Fejér monotone algorithms, this project expects to develop the m ....Nonmonotone Algorithms in Operator Splitting, Optimisation and Data Science. This project aims to develop the mathematical foundations for the analysis and development of optimisation algorithms used in data science. Despite their now ubiquitous use, machine learning software packages routinely rely on a number of algorithms from mathematical optimisation which are not properly understood. By moving beyond the traditional realms of Fejér monotone algorithms, this project expects to develop the mathematical theory required to rigorously justify the use of such algorithms and thereby ensure the integrity of the decision tools they produce. This mathematical framework is also expected to produce new algorithms for optimisation which benefit consumers of data science such as the health-care and cybersecurity sectors.Read moreRead less
Optimising progress towards elimination of malaria. The project aims to advance mathematical knowledge by developing novel tools appropriate for modelling disease elimination. We will apply these new mathematical tools to the significant problem of malaria elimination in Vietnam. The expected outcomes are new tools for modelling disease elimination on a fine spatial resolution with heterogeneities in individual patient characteristics, calibrating models to household level data on disease transm ....Optimising progress towards elimination of malaria. The project aims to advance mathematical knowledge by developing novel tools appropriate for modelling disease elimination. We will apply these new mathematical tools to the significant problem of malaria elimination in Vietnam. The expected outcomes are new tools for modelling disease elimination on a fine spatial resolution with heterogeneities in individual patient characteristics, calibrating models to household level data on disease transmission and designing intervention strategies for maximum effect on disease transmission. The innovative combination of modelling, inference and optimisation ensures that the mathematical methods developed will be broadly applicable to modelling elimination strategies for other infectious diseases.
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Determining features that separate groups of protein sequences. This project aims to develop mathematical approaches for determining features that distinguish one group of proteins from another, based on their amino acid sequences. The groups of sequences will reflect different outcomes, so that identifying the fundamental features can result in targeted interventions against the poorer outcome. A simple comparison at each position or of known features can fail to determine robust differentiator ....Determining features that separate groups of protein sequences. This project aims to develop mathematical approaches for determining features that distinguish one group of proteins from another, based on their amino acid sequences. The groups of sequences will reflect different outcomes, so that identifying the fundamental features can result in targeted interventions against the poorer outcome. A simple comparison at each position or of known features can fail to determine robust differentiators and so more complex methods are required. The project will, for example, help identify HIV vaccine targets by comparing early HIV transmission sequences from those in chronic infection. The methods will be applicable to viral proteins where high mutation rates make this task even more complex.Read moreRead less
Industrial Transformation Training Centres - Grant ID: IC200100009
Funder
Australian Research Council
Funding Amount
$4,861,236.00
Summary
ARC Training Centre in Optimisation Technologies, Integrated Methodologies, and Applications (OPTIMA). OPTIMA addresses industry’s urgent need for decision-making tools for global competitiveness: reducing lead times, and financial and environmental costs, while improving efficiency, quality, and agility. Despite strong expertise in academia, industry is yet to fully benefit from optimisation technology due to its high barrier to entry. Connecting industry partners with world-leading interdiscip ....ARC Training Centre in Optimisation Technologies, Integrated Methodologies, and Applications (OPTIMA). OPTIMA addresses industry’s urgent need for decision-making tools for global competitiveness: reducing lead times, and financial and environmental costs, while improving efficiency, quality, and agility. Despite strong expertise in academia, industry is yet to fully benefit from optimisation technology due to its high barrier to entry. Connecting industry partners with world-leading interdisciplinary researchers and talented students, OPTIMA will advance an industry-ready optimisation toolkit, while training a new generation of industry practitioners and over 120 young researchers, vanguarding a highly skilled workforce of change agents for transformation of the advanced manufacturing, energy resources, and critical infrastructure sectors.Read moreRead less
Real-time scheduling of trains to control peak electricity demand. This project aims to develop new scheduling and control methods that will enable railways to reduce their demand for electricity during peak demand periods, without undue disruption to the timetable.
These new methods and systems will integrate with—and expand the capabilities of—an Australian train control system that is used by railways around the world. This will enable better management of electricity within a region and be ....Real-time scheduling of trains to control peak electricity demand. This project aims to develop new scheduling and control methods that will enable railways to reduce their demand for electricity during peak demand periods, without undue disruption to the timetable.
These new methods and systems will integrate with—and expand the capabilities of—an Australian train control system that is used by railways around the world. This will enable better management of electricity within a region and better use of renewable energy sources, with significant cost savings for railways and the wider community.Read moreRead less
New mathematics for multi-extremal optimization and diffusion tensor imaging. This project aims to establish numerically certifiable mathematical theory and methods for semi-algebraic optimisation problems. Numerically certifiable optimisation principles and techniques are vital for the practical use of optimisation technologies because they can be readily implemented by common computer models and algorithms. Yet no such methodologies exist for multi-extremal, semi-algebraic optimisation problem ....New mathematics for multi-extremal optimization and diffusion tensor imaging. This project aims to establish numerically certifiable mathematical theory and methods for semi-algebraic optimisation problems. Numerically certifiable optimisation principles and techniques are vital for the practical use of optimisation technologies because they can be readily implemented by common computer models and algorithms. Yet no such methodologies exist for multi-extremal, semi-algebraic optimisation problems which are common in modern science and medicine. The expected outcomes of this project include enhanced optimisation methods for diffusion tensor imaging, an emerging technology in brain sciences.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE190101416
Funder
Australian Research Council
Funding Amount
$329,538.00
Summary
The value of model complexity for fisheries management. This project aims to quantify the benefits of using dynamic multi-species models for harvest decisions in the fishing industry. More than 99.8 per cent of fisheries are assessed using single-species models. Since fishers harvest multiple interacting species, not considering these interactions can lead to negative outcomes that reduce food security, eliminate human livelihoods, decrease economic production, and harm the environment. The proj ....The value of model complexity for fisheries management. This project aims to quantify the benefits of using dynamic multi-species models for harvest decisions in the fishing industry. More than 99.8 per cent of fisheries are assessed using single-species models. Since fishers harvest multiple interacting species, not considering these interactions can lead to negative outcomes that reduce food security, eliminate human livelihoods, decrease economic production, and harm the environment. The project is expected to provide guidance for fisheries scientists on when to use multi-species models for management, improved decision making capacity to reduce the risk of fishery collapse, a new method for dynamic model validation in the face of limited data, and enhanced collaboration between modellers and applied agencies. By reducing the risk of ecosystem collapse through better use of complex and simple models. The project will provide major benefits for the environment, humans, and the economy, at national and global scales.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