Mathematical and computational models for agrichemical retention on plants. Mathematical and computational models for agrichemical retention on plants. This project aims to build interactive software that simulates agrichemical spraying for multiple virtual plants reconstructed from scanned data. Mathematical modelling and computer simulation could offer an alternative to expensive experimental programs for agrichemical spraying of plants. This project will use contemporary fluid mechanics to bu ....Mathematical and computational models for agrichemical retention on plants. Mathematical and computational models for agrichemical retention on plants. This project aims to build interactive software that simulates agrichemical spraying for multiple virtual plants reconstructed from scanned data. Mathematical modelling and computer simulation could offer an alternative to expensive experimental programs for agrichemical spraying of plants. This project will use contemporary fluid mechanics to build practical mathematical models for droplet impaction, spreading and evaporation on leaf surfaces, and experimentally calibrate and validate the models. The software is expected to drive the development of agrichemical products that increase retention, minimise environmental impacts, and reduce costs for end-users.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE230100473
Funder
Australian Research Council
Funding Amount
$410,154.00
Summary
Effective integration of human and automated analyses for security testing. This DECRA project aims to significantly improve the performance of current state-of-the-art automated security testing approaches, enabling them to discover more security bugs in strict time constraints. The key innovation of the project is its novel way to embrace human element to leverage the ingenuity of the developers. This project will help companies improve the security and reliability of their products, thwarting ....Effective integration of human and automated analyses for security testing. This DECRA project aims to significantly improve the performance of current state-of-the-art automated security testing approaches, enabling them to discover more security bugs in strict time constraints. The key innovation of the project is its novel way to embrace human element to leverage the ingenuity of the developers. This project will help companies improve the security and reliability of their products, thwarting cyberattacks that cost Australian business $29 billion each year. The knowledge from this project will be transferred and integrated into higher education subjects to train the next generations of software developers, who are responsible to build security-critical systems that we all rely on now and in the future.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE240100040
Funder
Australian Research Council
Funding Amount
$442,302.00
Summary
Quality Assurance of Mobile Applications by Effective Testing and Repair. This project aims to create advanced techniques that will enable software engineers to effectively develop quality assured and robust software systems. This project expects to generate new and innovative approaches that automate software testing and repair. The expected outcomes of this project include new knowledge of software engineering, development of an automated and cost-effective testing system with improved coverag ....Quality Assurance of Mobile Applications by Effective Testing and Repair. This project aims to create advanced techniques that will enable software engineers to effectively develop quality assured and robust software systems. This project expects to generate new and innovative approaches that automate software testing and repair. The expected outcomes of this project include new knowledge of software engineering, development of an automated and cost-effective testing system with improved coverage, greater bug detection and repair, and faster testing protocols. This should provide significant benefits to software users by providing reliable and user-friendly systems and to software companies to position Australia as a global leader in software development and technological advancement.Read moreRead less
Mapping the Effectiveness of Automated Software Testing. This project aims to help software engineers build complex software systems in far more reliable and cost-effective ways. It takes an interdisciplinary approach by applying machine learning techniques to automatically test complex software systems. Expected outcomes include a novel methodology for assessing the strengths and weaknesses of test suites generated by automated software testing techniques and the approaches required for generat ....Mapping the Effectiveness of Automated Software Testing. This project aims to help software engineers build complex software systems in far more reliable and cost-effective ways. It takes an interdisciplinary approach by applying machine learning techniques to automatically test complex software systems. Expected outcomes include a novel methodology for assessing the strengths and weaknesses of test suites generated by automated software testing techniques and the approaches required for generating high-quality test cases. Such advances are urgently needed to avoid disasters when deploying software systems in the real world.Read moreRead less
Australian Laureate Fellowships - Grant ID: FL190100035
Funder
Australian Research Council
Funding Amount
$3,009,457.00
Summary
Human-centric Model-driven Software Engineering. This project aims to find fundamentally new ways to capture and use human-centric software requirements during model-driven software engineering and verifying that systems meet these requirements. There are major issues with misaligned software applications in terms of accessibility, usability, emotions, personality, age, gender, and culture. This project aims to address these through new conceptual foundations and modelling techniques for their s ....Human-centric Model-driven Software Engineering. This project aims to find fundamentally new ways to capture and use human-centric software requirements during model-driven software engineering and verifying that systems meet these requirements. There are major issues with misaligned software applications in terms of accessibility, usability, emotions, personality, age, gender, and culture. This project aims to address these through new conceptual foundations and modelling techniques for their support during software engineering. The intended outcomes are enhanced theory, models, tools and capability for next-generation software engineering with these critical elements. Significant benefits are expected to include greatly improved software quality, developer productivity and cost savings.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE240100144
Funder
Australian Research Council
Funding Amount
$444,447.00
Summary
Universal Model Selection Criteria for Scientific Machine Learning. This project aims to develop provably reliable universal model selection criteria to facilitate trustworthy scientific machine learning. Combining stochastic methods with an innovative geometric approach to basic statistical principles, this project expects to characterise, combine, and refine the most successful heuristics for designing and training huge models, such as deep neural networks, into a cohesive theoretical framewor ....Universal Model Selection Criteria for Scientific Machine Learning. This project aims to develop provably reliable universal model selection criteria to facilitate trustworthy scientific machine learning. Combining stochastic methods with an innovative geometric approach to basic statistical principles, this project expects to characterise, combine, and refine the most successful heuristics for designing and training huge models, such as deep neural networks, into a cohesive theoretical framework. The expected outcomes include a general toolkit for assisting neural network design at the forefront of scientific applications. This should significantly improve the quality of scientific predictions by facilitating confident adoption of deep learning methods into the pantheon of trustworthy modeling techniques. Read moreRead less
Values-oriented Defect Fixing for Mobile Software Applications. This project aims to address critical problems with mobile applications that exhibit human values-based defects, by advancing our understanding, detection and fixing of such defects. Many mobile apps do not operate according to the essential values of their human users - e.g. inclusivity, accessibility, privacy, ethical behaviour, due care, emotions, etc - making them ineffective, underused, unfit for purpose or even dangerous. Exp ....Values-oriented Defect Fixing for Mobile Software Applications. This project aims to address critical problems with mobile applications that exhibit human values-based defects, by advancing our understanding, detection and fixing of such defects. Many mobile apps do not operate according to the essential values of their human users - e.g. inclusivity, accessibility, privacy, ethical behaviour, due care, emotions, etc - making them ineffective, underused, unfit for purpose or even dangerous. Expected outcomes include new theories, techniques and prototype tools for developers and end users to detect and help fix values-based defects in mobile apps. Benefits include better, safer mobile apps for people and organisations and improved app developer productivity and competitiveness.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE220101057
Funder
Australian Research Council
Funding Amount
$424,140.00
Summary
Practical Automated Software Bug Fixing via Syntactic and Semantic Analyses. This proposal aims to advance the practical adoption of automated software bug repair, which has recently been adopted by industry, e.g., Facebook. It will produce novel methods that use mining software repositories, program analysis, and human-guided search to help automated repair to scale and be accurate. Expected outcomes include a publicly available automated bug repair framework. This project will help the softwar ....Practical Automated Software Bug Fixing via Syntactic and Semantic Analyses. This proposal aims to advance the practical adoption of automated software bug repair, which has recently been adopted by industry, e.g., Facebook. It will produce novel methods that use mining software repositories, program analysis, and human-guided search to help automated repair to scale and be accurate. Expected outcomes include a publicly available automated bug repair framework. This project will help the software industry deliver to users high quality software with improved reliability and safety, and increase education quality for students learning to code via automated feedback generation.Read moreRead less
Scalable Stream Processing in Hybrid Edge-Cloud Infrastructures. This project aims to develop a new computational paradigm to ensure low-latency services for streaming applications across heterogeneous Edge devices while satisfying high-throughput and scalability requirements. This project is of high significance for generating new knowledge in the area of real-time streaming using innovative algorithms that overcome the limitations of remote Cloud and distributed Edge computing. Expected outcom ....Scalable Stream Processing in Hybrid Edge-Cloud Infrastructures. This project aims to develop a new computational paradigm to ensure low-latency services for streaming applications across heterogeneous Edge devices while satisfying high-throughput and scalability requirements. This project is of high significance for generating new knowledge in the area of real-time streaming using innovative algorithms that overcome the limitations of remote Cloud and distributed Edge computing. Expected outcomes include novel programming abstractions, performance models, and control mechanisms to address complex problems for incremental and iterative computations in hybrid Edge-Cloud infrastructures. This should provide significant benefits, one of which is the optimised utilisation of limited computing resources.Read moreRead less
Phylodynamics for Single Cell Genomics . This project generates the mathematical framework required to look at single cell data in developmental systems and tissues. All cells in a multi-cellular organism derive from a single ancestral cell, generally the fertilised egg cell. Phylodynamics provides a framework to analyse and model this data, by connecting the shared ancestry of cells in an organism to the cell population and tissue dynamics. By developing the mathematical and statistical foundat ....Phylodynamics for Single Cell Genomics . This project generates the mathematical framework required to look at single cell data in developmental systems and tissues. All cells in a multi-cellular organism derive from a single ancestral cell, generally the fertilised egg cell. Phylodynamics provides a framework to analyse and model this data, by connecting the shared ancestry of cells in an organism to the cell population and tissue dynamics. By developing the mathematical and statistical foundations for the analysis of single cell data in a phylodynamic framework we will establish a powerful new computational tools for the analysis of tissues and developmental processes. Read moreRead less