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Transforming clinical pathways for abdominal aortic aneurysm through use of blood and imaging biomarkers
Transforming clinical pathways for abdominal aortic aneurysm through use of blood and imaging biomarkers
Colonial Commemorative Landscapes in Australia
This project contributes to the team’s research program relating to historical memory and digital mapping. Stevenson and Brennan have complementary strengths and seek to build their collaborative capacity towards a future ARC DP application. Stevenson is a feminist historian of social movements with prior experience in the digital humanities through The Suffrage Postcard Project, a digital archive of transatlantic suffrage postcards from the 1910s. As a PI on a current ARC Discovery Project about the history of archiving social movements, she is working on historical memory and digital archiving. Brennan is an environmental historian, specialising in European exploration and digital history. Brennan’s work on the Coral Discovery Project involves digitally mapping all the European scientific voyages to the Pacific between 1768 and 1834. This new research project builds on their co-authored journal article (Minor Revisions) under consideration with a Radical History Review Special Edition: “Memory Over Forgetting: Monuments, Memorials and Intangible Heritage.”
The team’s new project aligns directly with the Centre for Heritage and Culture theme of “storied landscapes.” Digitally mapping colonial commemorative sites strengthens the CHC’s strategic research about storied landscapes. The team’s previous map of Cook monuments revealed that Captain Cook has become an indelible figure in Australia’s commemorative landscape with little connection between sites of historical significance and commemorative infrastructure. This method of digital historical inquiry enables the team to uncover, interpret, and translate the past to create new stories about the history and legacy of colonial figures. Digital mapping will allow the team to clearly tease out the tensions between historical knowledge and historical memory across these storied landscapes.
Fusion of wearable and environmental sensors for remote monitoring of health and wellbeing in elderly populations
This project is funded by the Northern Australia Regional Digital Health Collaborative (NARDHC). This project aims to develop a smart home health monitoring prototype that improves upon existing technology by fusing information from multiple sensors. The proposed system will use non-invasive wearable sensors, non-contact mmWave technology, and artificial intelligence to monitor key vital signs, physical activity, stress, fatigue, and environmental conditions. The goal of this project is to prototype a comprehensive system for monitoring health and wellbeing in rural and remote Australia, with particular focus on elderly persons
Machine Learning Method for Measuring Blood Pressure and Monitoring Renal Perfusion Non-Invasively in the Neonatal Intensive Care Unit
This project firstly aims to develop machine learning algorithms capable of continuously monitoring blood pressure in babies born very preterm, using heart activity waveforms obtained from low-cost and non-invasive photoplethysmogram and electrocardiogram sensors. The second aim is the development of machine learning algorithms for early identification of acute kidney injury risk and early diagnosis when it does occur. It is expected that this work will provide non-invasive alternatives for measuring key neonatal health parameters, in turn leading to improved patient outcomes. This This work also has significant potential to support critical care in low-resource and remote areas.
A mobile app and dashboard for effective management of early-stage chronic kidney disease
This project is funded by the Northern Australia Regional Digital Health Collaborative (NARDHC). The incidence and prevalence of chronic kidney disease (CKD) varies globally, and people in the lowest socioeconomic quartile have a 60% higher risk of progressive CKD. This project aims to develop a mobile app that detects vulnerable individuals who are at risk of deterioration in renal function and are needing intervention, while also allowing monitoring and appropriate education to those who are progressing steadily. The expected outcome is a novel mobiele analytic app that can improve the management of CKD patients in rural and remote areas for better health outcomes and planning.
Development of a machine learning tool for gap-filling cloudy satellite data for use in environmental science application
In this project, we developed an artificial intelligence tool for gap filling sea-surface temperatures in cloud-affected data. The results are now published in IEEE Transactions on Geoscience and Remote Sensing (IF: 8.2 in 2024)
Do you see what I see? Developing responsible Artificial Intelligences that explain their decisions in a manner consistent with human attention
In this project, I am developing a novel method for evaluating explainable AI methods that is based on how humans pay attention to images. This will assist future researchers in benchmarking new explainable AI tools.