College of Science & Engineering


Sophia Love

Sophia Love

Romain Vaucher

Romain Vaucher

Jenny Fisher

Jenny Fisher

Ambili Narayanan

Ambili Narayanan

Sarfaraz Ali

Sarfaraz Ali

Tom Lloyd

Tom Lloyd

An integrated multi-omics approach to expedite diagnosis and management of inborn errors of immunity
Matt Field
01 Jan 2024 - 01 Jan 2028
Australia wide grant to develop national program to identify and manage nborn errors of immunity using multi-omic techologies
Transforming clinical pathways for abdominal aortic aneurysm through use of blood and imaging biomarkers
Matt Field
01 Jan 2023 - 01 Jan 2026
Transforming clinical pathways for abdominal aortic aneurysm through use of blood and imaging biomarkers
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)
Reseracher: Michael Bird (Distinguished Professor)
Start Date: 01 Jan 1989
End Date: 01 Jan 1990
Start Date: 01 Jan 2017
Reseracher: Paul Horwood (Professor, Promotional Chair)
Start Date: 01 Jan 2017
Reseracher: Paul Horwood (Professor, Promotional Chair)
Start Date: 01 Jan 2019
Start Date: 01 Jan 2018
Reseracher: Paul Horwood (Professor, Promotional Chair)
Start Date: 01 Jan 2012
Reseracher: Paul Horwood (Professor, Promotional Chair)
Start Date: 01 Jan 2010
Start Date: 01 Jan 2019