Honours and Masters project

Pretrained models

The hidden layers of pretrained foundation models, such as ChatGPT, contain useful and abstract summaries of data. From an information-theoretic perspective, they might compress the data. From a machine learning perspective, they compute useful features of the data. From a statistics perspective, they might be sufficient statistics for a parameter of interest.

Probabilistic models

This project involves building a system that processes IoMT data(such as heart rate, blood pressure, or glucose levels) from wearable devices to monitor patient health in real time.

The system uses machine learning to detect anomalies and alert healthcare providers or caregivers. It includes data preprocessing, model training, and a simple dashboard for visualization.

This project aims to develop a modular and distributed digital twin framework focused on simulating, monitoring, and analysing smart grid data. The framework will represent virtual models of grid components (e.g., loads, meters, nodes) and synchronise them with real-time or simulated data streams using distributed systems principles.

The testbed will support:

This is a research and development project focused on designing a Generative AI tool that supports equitable team practices in software development. The project combines qualitative research, such as persona profile creation, with AI prototyping to explore how GenAI can foster inclusion, improve team dynamics, and accommodate diverse working styles in technical environments. The outcome includes both a functional AI prototype and practical resources for inclusive collaboration.

This project focuses on implementing an AI-powered digital twin for intelligent electric vehicle (EV) traffic management in smart cities, utilising the YOLO algorithm. It develops a basic digital twin system designed to monitor and manage EV traffic in urban areas. The system detects and tracks EVs using feeds from traffic cameras. The digital twin simulates traffic flow, providing a visualisation of EV movement, congestion points, and route patterns.

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).