{"links":{"self":"http://dataportal.arc.gov.au/NCGP/API/grants/DE260101486"},"data":{"type":"grant-details","id":"DE260101486","attributes":{"code":"DE260101486","administering-organisation":"The University of Sydney","announcement-administering-organisation":"The University of Sydney","scheme-name":"Discovery Early Career Researcher Award","grant-status":"Active","funding-commencement-year":2026,"years-funded":3,"project-start-date":"2026-12-07","anticipated-end-date":"2029-12-06","grant-summary":"Practical Deep Learning for Physiological Time Series Analysis. With deep learning’s success, physiological time series analysis still faces significant challenges due to limited training data, difficulty in adapting to new domains, and black-box models. This project aims to pioneer practical deep learning methods that are consistently effective, even when faced with label scarcity, domain shift, and explainability gaps. The expected outcomes of this project are technological breakthroughs in sequential data analysis, using novel self-supervised, adaptive, and interpretable methods.  These advancements will lay solid theoretical foundations for a broad range of time series applications, including human-centered manufacturing, human-machine interaction, Internet of Things, and smart cities.","funding-current":517457.00,"funding-at-announcement":513612,"investigators-current":[{"title":"Dr","firstName":"Xiang","familyName":"Zhang","roleName":"Discovery Early Career Researcher Award","roleCode":"DECRA","isFellowship":true,"orcidIdentifier":"0000-0001-5097-2113 "}],"investigators-at-announcement":[{"title":"Dr","firstName":"Xiang","familyName":"Zhang","roleName":"Discovery Early Career Researcher Award","roleCode":"DECRA","isFellowship":true,"orcidIdentifier":"0000-0001-5097-2113 "}],"organisations-current":[{"organisationName":"The University of Sydney","roleName":"Administering Organisation","state":"NSW"}],"organisations-at-announcement":[{"organisationName":"The University of Sydney","roleName":"Administering Organisation","state":"NSW"}],"field-of-research":[{"isPrimary":false,"code":"380205","name":"Time-Series Analysis","type":"FOR20"},{"isPrimary":false,"code":"460502","name":"Data Mining and Knowledge Discovery","type":"FOR20"},{"isPrimary":true,"code":"4611","name":"Machine Learning","type":"FOR20"},{"isPrimary":false,"code":"461103","name":"Deep Learning","type":"FOR20"}],"socio-economic-objective":[{"code":"220403","name":"Artificial Intelligence","type":"SEO20"},{"code":"280102","name":"Expanding Knowledge In the Biological Sciences","type":"SEO20"},{"code":"280112","name":"Expanding Knowledge In the Health Sciences","type":"SEO20"}],"international-collaboration":["Canada","Italy","United States of America"],"lief-register":[],"achievement-summary":null,"national-interest-test-statement":"This project aims to create advanced methods for analyzing physiological time series, which are signals such as heart rates or brain waves that reflect human bodily functions such as running. Current methods typically find it difficult to handle the complexity and variability of such data such as heart rates when feeling fatigued. This project focuses on developing innovative computational tools to resolve this. \n\nThis project could significantly enhance our understanding of the human body, offering new insights into biological functions such as how the brain works. In the longer term, this project lays theoretical foundations to enable Australians to monitor their health at home using affordable and portable devices. The proposed algorithm's adaptability extends to various sensory data types, offering environmental and commercial benefits. For example, it could predict forest fires using historical climate data, and detect mechanical issues via vibration analysis, enhancing community safety and industry innovation. Additionally, in cybersecurity, the proposed methods could enhance threat detection and strengthen Australia's digital security.\n\nTo maximize the impact, the outcomes could be promoted beyond academia to support practical scenarios such as safe manufacturing and the Rural Fire Service. "}}}