Honours and Masters project

This project develops a collaborative handover system using an augmented reality (AR) headset display to improve ICU nurse communication. It integrates in-situ AR visualisations and a cross-reality collaboration model to address cognitive challenges and data fragmentation in handovers. The prototype aims to enhance the accuracy and efficiency of handover for practical application in high-pressure settings.

This project builds upon an existing safety inspection system framework, integrating augmented reality (AR), artificial intelligence (AI), and automated reporting. You will follow the framework to design, implement, and evaluate a fully functioning prototype system that supports safety inspections in real-world construction environments.

The convergence of Artificial Intelligence (AI) with immersive technologies such as Augmented Reality (AR) and Virtual Reality (VR) is rapidly transforming the landscape of healthcare and medical training. While AI enhances decision-making through data-driven insights, AR and VR offer intuitive, spatial, and interactive environments that support diagnostics, education, therapy, and surgical planning. However, the integration of these technologies remains fragmented, with varying degrees of adoption, technical maturity, and clinical impact.

Which route is the best to drive from Monash University (Clayton campus) to Melbourne CBD? 

For many of us, answering this question would likely mean opening a route natvigation app and asking the provider to give us the fastest route. For some of us, this question might not need to be answered as you may already be experienced to drive from Monash Uni to CBD, or simply find that the route computed by the app is insufficent to handle your specific requirements, preferences, or constraints. 

In NeuroDistSys (NDS): Optimized Distributed Training and Inference on Large-Scale Distributed Systems, we aim to design and implement cutting-edge techniques to optimize the training and inference of Machine Learning (ML) models across large-scale distributed systems. Leveraging advanced AI and distributed computing strategies, this project focuses on deploying ML models on real-world distributed infrastructures, improving system performance, scalability, and efficiency by optimizing resource usage (e.g., GPUs, CPUs, energy consumption).

In SmartScaleSys (S3), we aim to design and build resource management solutions to learn from usage patterns, predict future needs, and allocate resources to minimize latency, energy consumption, and costs of running diverse applications in large-scale distributed systems. This project offers researchers and students a chance to explore cutting-edge concepts in AI-driven infrastructure management, distributed computing, and energy-aware computing, preparing them for impactful roles in industry and research.

This project will involve benchmarking state of the art methods for time series classification on the new MONSTER benchmark datasets [1, 2, 3].  Currently almost all benchmarking in time series classification is performed on the (almost all very small) datasets in the UCR and UEA archives.  This is particularly unsuitable for deep learning models which are low bias models and ideally trained using large quantities of data.  The "true" performance of current deep learning methods for time series classification is unknown outside of the UCR/UEA datasets.  Most deep learning models for times…

Politeness plays a pivotal role in fostering constructive, respectful communication and maintaining positive social dynamics in educational settings. In online learning environments—such as discussion forums on Moodle—language becomes the primary medium for interaction between instructors and students. While prior studies have highlighted the benefits of politeness in building rapport and encouraging engagement, limited empirical work has systematically examined how politeness is expressed by both instructors and students in these digital spaces.

Are you interested in biomedical? You could combine your data science and computing expertise to analyse DNA and genetics.