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
Pathways to agri-food supply chains that co-benefit people and nature. This project aims to improve biodiversity outcomes of agricultural food production and consumption, and expects to generate new knowledge about impacts of interventions and shocks on the environment, human health and livelihoods in agri-food systems. This will be achieved using an interdisciplinary approach that accounts for uncertainties in links between farmers, suppliers, consumers and supply-chain outcomes. The expected o ....Pathways to agri-food supply chains that co-benefit people and nature. This project aims to improve biodiversity outcomes of agricultural food production and consumption, and expects to generate new knowledge about impacts of interventions and shocks on the environment, human health and livelihoods in agri-food systems. This will be achieved using an interdisciplinary approach that accounts for uncertainties in links between farmers, suppliers, consumers and supply-chain outcomes. The expected outcome is a value of information framework for identifying nature-friendly policies and actions with co-benefits for human well-being. Benefits include sustainability pathways with win-win outcomes for people and nature, and improved ways of meeting international commitments such as Sustainable Development Goals.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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An intelligent machine modelling assistant for combinatorial optimisation. This project aims to discover key fundamental technologies for automating assistance to non-expert users in the formulation of mathematical models. Through automating the modelling of combinatorial optimization problems, this research will generate new knowledge to address the fundamental challenges of automatic mathematical modelling. This intelligent assistant will enable synthesis of new mathematical models through th ....An intelligent machine modelling assistant for combinatorial optimisation. This project aims to discover key fundamental technologies for automating assistance to non-expert users in the formulation of mathematical models. Through automating the modelling of combinatorial optimization problems, this research will generate new knowledge to address the fundamental challenges of automatic mathematical modelling. This intelligent assistant will enable synthesis of new mathematical models through the utilisation of pioneering natural language processing components and novel custom-made machine-readable knowledge bases. The outcome of this research will broaden access to high-quality models by non-expert workforce and alleviate the shortage of expert mathematicians, bringing significant social and economic benefits.Read moreRead less
Large scale nonsmooth, nonconvex optimisation. This project aims to develop, analyse, test and apply (sub) gradient-based methods for solving large scale nonsmooth, nonconvex optimisation problems. Large scale problems with complex nonconvex objective and/or constraint functions are among the most difficult in optimisation. This project will generate new knowledge in numerical optimisation and machine learning. The use of structures and sparsity of large scale problems will lead to the developme ....Large scale nonsmooth, nonconvex optimisation. This project aims to develop, analyse, test and apply (sub) gradient-based methods for solving large scale nonsmooth, nonconvex optimisation problems. Large scale problems with complex nonconvex objective and/or constraint functions are among the most difficult in optimisation. This project will generate new knowledge in numerical optimisation and machine learning. The use of structures and sparsity of large scale problems will lead to the development of better models, and more accurate and robust methods. The expected outcomes of the project are ready-to-implement and apply numerical methods for solving large-scale, nonsmooth, nonconvex optimisation problems, as well as problems in machine learning and regression analysis.Read moreRead less
Stable on-demand optimization for workforce and fleet logistics management. This project aims to conceive, develop and deploy innovative methodologies for stable on-demand workforce management and fleet logistics based on advanced decision-support systems. The outcome of this project will provide a new cloud-based real-time Optimisation Software-as-a-Service (OSaaS) platform that allows businesses to improve their productivity while reducing operating costs and their environmental footprint. Thi ....Stable on-demand optimization for workforce and fleet logistics management. This project aims to conceive, develop and deploy innovative methodologies for stable on-demand workforce management and fleet logistics based on advanced decision-support systems. The outcome of this project will provide a new cloud-based real-time Optimisation Software-as-a-Service (OSaaS) platform that allows businesses to improve their productivity while reducing operating costs and their environmental footprint. This is expected to support the manufacturing, retail, delivery and mobile fleets industries.Read moreRead less
Switching Dynamics Approach for Distributed Global Optimisation . This project aims to create a breakthrough switching dynamics approach and new technology to speed up finding optimal solutions. It will develop a distributed switching dynamics based optimisation scheme for global optimisation problems in industrial big-data environments where timely decision making is required. It will result in a practical technology for industry optimisation problems such as economic energy dispatch in smart g ....Switching Dynamics Approach for Distributed Global Optimisation . This project aims to create a breakthrough switching dynamics approach and new technology to speed up finding optimal solutions. It will develop a distributed switching dynamics based optimisation scheme for global optimisation problems in industrial big-data environments where timely decision making is required. It will result in a practical technology for industry optimisation problems such as economic energy dispatch in smart grids and optimal charging and discharging tasks in a large network of electric vehicles, helping Australian power industry improve efficiency and security, as well as training the next generation scientists and engineers for Australia in this emerging field.Read moreRead less
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