Honours and Masters project

Australia’s cybersecurity infrastructure, particularly in IoT networks, must be strengthened to meet evolving standards set by international bodies like NIST and the NSA. This project will support Australian organizations in adapting to quantum-safe standards, ensuring the protection of sensitive data and critical system

Access to education is an important issue. A major factor preventing access can be the cost of textbooks, which is a significant barrier for some students. Open Education Resources (OERs) are a popular option for reducing this financial burden, as they are free to any person with an internet connection. 

Manual medical report writing is time-consuming and subject to variability. Recent advances in large language models (LLMs) create new opportunities for automating this process. This project explores using LLMs to generate medical reports from a very large dataset, aiming to streamline workflows and support clinical decision-making. Students will work on data preprocessing, model fine-tuning, and performance evaluation, contributing to advances in medical AI.

Using artificial intelligence software and unique algorithms for predictive analytics that incorporate modelling, machine learning, and data mining, we analyse, model, and build an individual’s baseline health profile against thousands (eventually millions) of similar people and their data points, along with decades of evidence-based medical and population research. Our previous work focused on the prediction of Diabetes Type Two – a major debilitating chronic disease, and a significant contributor to global deaths.

As games have increased in complexity, so have the worlds in which players find themselves. The automap has become a staple feature of many video games; in some, the players is allowed to edit the map itself during play (e.g., to mark important locations or lay out a course). We know about what games offer players in terms of game maps, but not how players *actually use* those features. This project will collect data on players' cartography activities in games. 

Gender euphoria addresses times when one's lived experience aligns with their gender identity. This may be personal experiences of one's body, how one is treated by others, or through in-game experiences. Our prior research has developed an understanding of how gender euphoria comes through in video games from first-person research. This project will work toward developing and deploying a questionnaire to study this phenomenon in the wild and analyse the results. 

What is a mixture model?

You may have learned about mixture models in a machine learning or statistics course. A mixture model with K component densities is defined by

• a set of K nonnegative mixture weights summing to one, and
• a corresponding set of K nonnegative component densities, each of which integrates to one.

The sum of the product of the mixture weights and component densities is guaranteed to be nonnegative and integrates to one, meaning it is a valid probability density.

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

Leveraging the FLoRA adaptive learning platform, we will conduct a five-phase research program combining experimental studies and advanced trace data analysis. Through time-stamped interaction data, we aim to detect behavioural signals of metacognitive disengagement using machine learning and time-series modeling techniques. These insights will inform the development of adaptive scaffolding tools that encourage students to monitor, evaluate, and adjust their learning strategies when using GenAI.

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.