The Australian Research Data Commons (ARDC) invites you to participate in a short survey about your
interaction with the ARDC and use of our national research infrastructure and services. The survey will take
approximately 5 minutes and is anonymous. It’s open to anyone who uses our digital research infrastructure
services including Reasearch Link Australia.
We will use the information you provide to improve the national research infrastructure and services we
deliver and to report on user satisfaction to the Australian Government’s National Collaborative Research
Infrastructure Strategy (NCRIS) program.
Please take a few minutes to provide your input. The survey closes COB Friday 29 May 2026.
Complete the 5 min survey now by clicking on the link below.
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
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
Mathematics and computing for integrated stockyard-centric management of mining supply chains. Blended mineral products, such as coal and iron ore, make a strong contribution to Australia's economy. Blending occurs in stockpiles, so to realise product value, stockyard and supply chain operational plans must align with blend targets. This project will provide new mathematical and computational planning tools to maximise this value.