Australian Laureate Fellowships - Grant ID: FL170100006
Funder
Australian Research Council
Funding Amount
$3,016,065.00
Summary
Pattern analysis for accelerating scientific innovation. This project aims to determine how pattern recognition can be harnessed to accelerate and expand the capability of experimental optimisation that underpins scientific innovation. Disrupting current experimental methods, this new framework will use data-driven models to guide humans through experimental complexity. The expected outcomes of the project include advancing the theory and practice of pattern recognition in Bayesian optimisation ....Pattern analysis for accelerating scientific innovation. This project aims to determine how pattern recognition can be harnessed to accelerate and expand the capability of experimental optimisation that underpins scientific innovation. Disrupting current experimental methods, this new framework will use data-driven models to guide humans through experimental complexity. The expected outcomes of the project include advancing the theory and practice of pattern recognition in Bayesian optimisation by solving both fundamental and translatory problems, totally transforming the way complex experimental explorations can be done. The project will establish Australia as a leader in innovation-led productivity in the 4th industrial revolution, which will include ground-breaking investigations into the use of pattern recognition to navigate complexity in the experimental process.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
Australian Laureate Fellowships - Grant ID: FL140100012
Funder
Australian Research Council
Funding Amount
$2,830,000.00
Summary
Stress-testing algorithms: generating new test instances to elicit insights. Stress-testing algorithms: generating new test instances to elicit insights. This project aims to develop a new paradigm in algorithm testing, creating novel test instances and tools to elicit insights into algorithm strengths and weaknesses. Such advances are urgently needed to support good research practice in academia, and to avoid disasters when deploying algorithms in practice. Extending our recent work in algorith ....Stress-testing algorithms: generating new test instances to elicit insights. Stress-testing algorithms: generating new test instances to elicit insights. This project aims to develop a new paradigm in algorithm testing, creating novel test instances and tools to elicit insights into algorithm strengths and weaknesses. Such advances are urgently needed to support good research practice in academia, and to avoid disasters when deploying algorithms in practice. Extending our recent work in algorithm testing for combinatorial optimisation, described as 'ground-breaking,' this project aims to tackle the challenges needed to generalise the paradigm to other fields such as machine learning, forecasting, software testing, and other branches of optimisation. An online repository of test instances and tools aim to provide a valuable resource to improve research practice and support new insights into algorithm performance.Read moreRead less