{"links":{"self":"http://dataportal.arc.gov.au/NCGP/API/grants/DE260101588"},"data":{"type":"grant-details","id":"DE260101588","attributes":{"code":"DE260101588","administering-organisation":"The University of Melbourne","announcement-administering-organisation":"The University of Melbourne","scheme-name":"Discovery Early Career Researcher Award","grant-status":"Active","funding-commencement-year":2026,"years-funded":3,"project-start-date":"2026-01-01","anticipated-end-date":"2028-12-31","grant-summary":"Enhancing Adaptive Intervention with Bayesian Methods for Complex Data. This project aims to develop next-generation adaptive intervention methods for tackling complex real-world data challenges by leveraging modern Bayesian techniques. Existing methods cannot quantify uncertainty and struggle with critical issues like interference, unmeasured confounders, non-stationarity that leads to suboptimal decisions. The project will establish theoretically sound and flexible statistical tools for supporting timely adaptive decision-making that optimises a long-term objective. Expected outcomes include new statistical methods and software to handle evolving situations in challenging settings. This research promises societal and economic benefits in policy-making for public health and medicine.","funding-current":509949.00,"funding-at-announcement":506104,"investigators-current":[{"title":"Dr","firstName":"Weichang","familyName":"Yu","roleName":"Discovery Early Career Researcher Award","roleCode":"DECRA","isFellowship":true,"orcidIdentifier":"0000-0002-0399-3779 "}],"investigators-at-announcement":[{"title":"Dr","firstName":"Weichang","familyName":"Yu","roleName":"Discovery Early Career Researcher Award","roleCode":"DECRA","isFellowship":true,"orcidIdentifier":"0000-0002-0399-3779 "}],"organisations-current":[{"organisationName":"The University of Melbourne","roleName":"Administering Organisation","state":"VIC"}],"organisations-at-announcement":[{"organisationName":"The University of Melbourne","roleName":"Administering Organisation","state":"VIC"}],"field-of-research":[{"isPrimary":true,"code":"4905","name":"Statistics","type":"FOR20"},{"isPrimary":false,"code":"490503","name":"Computational Statistics","type":"FOR20"},{"isPrimary":false,"code":"490508","name":"Statistical Data Science","type":"FOR20"},{"isPrimary":false,"code":"490509","name":"Statistical Theory","type":"FOR20"}],"socio-economic-objective":[{"code":"280118","name":"Expanding Knowledge In the Mathematical Sciences","type":"SEO20"}],"international-collaboration":["Korea, Republic of (South)","Singapore","United States of America"],"lief-register":[],"achievement-summary":null,"national-interest-test-statement":"This project aims to develop new statistical methods for analysing complex longitudinal data to facilitate policy adaptation and personalised intervention, enhancing the outcomes of AI-guided decision-making. The resulting new knowledge in statistical modelling for adaptive decision-making will benefit diverse fields, such as healthcare, where it can prevent disease severity deterioration by determining the best treatment for each person according to their evolving health status, social, and environmental factors. This will deliver social and economic benefits for Australia, improving community health and reducing treatment costs. The methods will also help ecologists preserve Australia’s natural habitats, allowing them to adapt strategies according to each location’s volatile climate condition. This will provide environmental and economic benefits by improving collection, interpretation and sharing of monitoring data, supporting cost-effective, high-impact environmental decisions and actions. Proactive outreach efforts with industry, policymakers, clinicians, and collaborators will be conducted to maximise the impact and application of the research findings."}}}