Posts by Michelle Luca

AI motion capture system for enhancing human motor function

This project aims to advance the field of human movement science by addressing the challenges encountered when developing a low-cost, automated system for screening the movement of pre-elite student-athletes. Leveraging state-of-the-art artificial intelligence (AI), markerless motion capture and stereo vision technologies, this research will tackle critical challenges in biomechanics and sports science.

Influencing factors of feed intake and digestion in prawns

This Project will investigate poorly understood mechanisms regulating feed consumption in prawns through a multi-disciplinary approach studying feeding behaviour, digestive physiology, nutritional needs and metabolism regulation. The expected outcomes are to better understand dietary factors and physiological mechanisms promoting feed consumption and return of appetite in juvenile prawns. This may lead to the application of new feed and feeding strategies to enhance aquaculture performance and sustainability.  

Optimizing carcase quality and animal welfare

This Project will investigate stunning factors in commercial beef processing facilities that may influence carcase quality, meat quality and/or animal welfare. The expected outcomes are to identify risk factors associated with stunning that influence meat quality and to support the claim that the Australian beef processing sector is continuing to improve welfare outcomes. This could provide evidence to bolster the ongoing social acceptance of the beef industry.

Prediction of molecular interactions with RNA using AI

This Project will develop deep-learning models to predict interactions of ribonucleic acid (RNA) with other molecules. The expected outcomes are to improve prediction capabilities to decode RNA interactions in disease mechanisms, identify novel therapeutic modalities, and improve existing therapies for targeting RNA. This could result in enhanced capacity to design new therapies and potential to optimise RNA targeting molecules for therapeutic applications. 

Applying imaging methods and data analytics to explore the listening brain

This Project aims to understand brain circuits and processes supporting communication in individuals with hearing problems, including those who use devices such as hearing aids and cochlear implants. The potential benefits are that individualised strategies based on real-time brain states estimate algorithms to empower listening and support effective communication. The Project will use brain-imaging techniques‚ including those compatible with listening technologies, including electroencephalogram (EEG), to explore the listening brain. The Project will explore brain changes that arise from hearing loss, how changes in brain function – within and beyond the auditory brain – arise to support listening when hearing is impaired, and how these findings can be used as a part of devices such as cochlear implants that engage the rest of the brain to support an individual's listening. 

Clinical lab automation with AI human robot interface

This Project will develop an AI-based robotic programming interface based on large-language model that allows practitioners, regardless of their technical expertise, to efficiently program and control robots. The expected outcomes are to improve efficiency in designing and deploying clinical lab automation and to expand the use of robotics within laboratories. This may lead to improvements with workflow for clinical lab automation, particularly during high-demand situations like pandemic outbreaks.

Quantifying methane emissions from wastewater treatment

This project aims to quantify methane emissions from wastewater treatment plants. The expected outcomes are improved understanding of methane emissions from within the plant, their spatial and temporal variability, and how they contribute to the total emissions. This may reduce emissions of methane.  

Cyber security risk mitigation for sensor data integrity

This Project will investigate cyber security risk mitigation approaches to secure the integrity of sensor information feeding into critical infrastructure operational systems and digital twins. The expected outcome is the development of guidelines for implementing robust cyber security measures. This may enhance resilience against cyber threats and ensure the integrity of decision-making processes.​

Predictive farming 

This Project will investigate artificial intelligence (AI) to improve weather forecasts and use crop models for making better farming decisions. The expected outcome is protocols for integrating AI-enhanced weather forecasts and crop models into farm management and planning tools. This is expected to result in farmers making more informed decisions that optimise resource use, boost crop yields, and enhance overall farm productivity and profitability while minimising environmental impact. 

Satellite-based Methane Detection

Methane is a potent greenhouse gas and an important contributor to climate change. This project will develop neural network-based methods to detect anthropogenic methane plumes in satellite imagery and quantify emission rates. The expected outcomes are better detection and monitoring of methane emissions in Australia compared to current methods, with enhanced temporal and spatial coverage. These advancements will enhance Australia’s capability to efficiently identify, quantify and mitigate methane emissions.