Honours and Masters project

Large Language Models (LLMs) are increasingly used as AI agents that can remember information across sessions, use tools, and take actions on behalf of users. For example, an AI agent may remember a user’s preferred contacts, coding conventions, project notes, research rules, or previous task instructions. This long-term memory can make agents more useful and personalised, especially for tasks that continue over time. However, long-term memory also introduces new security risks. An agent may remember

Large Language Model (LLM) agents are increasingly extended through external skills, tools, plugins, and reusable instruction modules. These skills allow agents to perform complex tasks such as file processing, web search, code execution, data analysis, and workflow automation. However, current security checks usually evaluate each skill in isolation. A skill may appear benign when tested alone, but become harmful when combined with other skills in the same agent workflow.

This project is an exciting opportunity to work on a multidisciplinary initiative aimed at improving the efficiency and effectiveness of energy control room operations. By focusing on human factors, best practices in visualisation, decision support, explainable AI, and optimisation, this project explores how to present information effectively for control room operators, ultimately contributing to the stability and efficiency of energy grid operations.

Background:

In our research we are reimagining the Control Room of the Future, where advanced data tools support better decision-making in complex environments like energy grid operations. A key focus of our research is understanding how operators interact visually with large-scale information displays. To do this, we use eye-tracking technology to capture detailed visual attention patterns, and synchronise it with workstation-level video and researcher notes. 

Agriculture is a significant contributor to nitrogen and phosphorus in waterways, impacting public health and ecosystems. Understanding and communicating the complex processes governing nutrient exports from agricultural catchments—regions where water collects and drains into a common outlet—are crucial for effective management and policy development. 

This project seeks to explore and trial new map morphing representations for seeing river water quality data sets more effectively over time and space. 

We are particularly focusing on the Melbourne and the region of Victoria, but expect the visualisation to be applicable to any geographical region.  

This project uses machine learning and predictive analytics to support the early detection and risk prediction of breast cancer using publicly available or clinical healthcare datasets. Students will clean and analyse clinical and diagnostic health data, apply classification and risk modeling algorithms such as Logistic Regression, Random Forest, Support Vector Machines, or deep learning, and identify key clinical risk factors and diagnostic markers.

 Title: Health and Social Challenges of Refugee Populations in Australia: A Data-Driven Investigation.

Keywords: Refugees, health outcomes, social challenges, data integration, policy analysis

Project Description: Perform a comprehensive data-driven study on health and social challenges faced by refugee populations in Australia. The project integrates multiple datasets, applies advanced statistical analysis, and uses machine learning to detect patterns that inform policy and support services.

Background

Diabetes can lead to serious complications, including peripheral neuropathy, peripheral arterial disease and diabetic foot disease, which may increase the risk of foot ulcers and lower-limb amputation. Early identification of high-risk patients can support timely clinical intervention and preventive care.

Research Aim

Background

Endometriosis and Polycystic Ovary Syndrome (PCOS) are complex conditions that can involve diverse symptoms and clinical characteristics. Delays in diagnosis and differences in symptom presentation highlight the potential value of data-driven approaches to support earlier identification and risk assessment.

Research Aim