Honours and Masters project

🎯 Research Motivation

While many AI-powered coding assistants (e.g., GitHub Copilot, ChatGPT Code Interpreter) improve coding productivity, they are not optimized for pedagogical impact. CS students need not just code completion but understanding, feedback, and guidance that nurtures problem-solving and conceptual mastery.

Your research could bridge this gap by designing an AI IDE extension that acts as a mentor, dynamically adapting its feedback to the learner’s skill level, learning style, and progress.

Access to visual information, such as information graphics, is compromised for people who are blind or have low vision (BLV). Access is typically provided through written or verbal descriptions, or tactile graphics. These, however, are often provided by specialist producers which takes time and reduces the agency of the person for when they get the alternate format and also the ability to make their own interpretations.

Access to cultural institutions, such as galleries and museums, is often compromised for people with disability. This includes people who are blind or have low vision (BLV). This project seeks to improve experiences within cultural institutions such as galleries and museums for BLV people, by applying AI and human-centred design principles to the creation of mediating artefacts and experiences.

This project is a multidisciplinary project between human-centred computing and data visualisation experts and water engineer experts in engineering and chemistry exploring new and immersive visual communication of complex ecosystems.

Smart glasses that combine mixed reality head-mounted displays with computer vision and natural language understanding, such as the Apple VisionPro or Google XR Glass, have the potential to revolutionise the lives of people with low vision by providing access to information about their environment through augmented vision and audio.

Modern clinical decision-making is constrained by associative models that conflate correlation with causation and overlook interactions among patient factors. This project introduces a unified framework that fuses causal inference with graph neural networks to deliver interpretable, high-precision diagnosis. Using electronic health records, Double Machine Learning isolates causal drivers (e.g., treatment effects, biomarkers) from spurious associations while adjusting for confounders such as socioeconomic status.

We will design a multi-agent web agent system (powered by LLMs) capable of understanding natural language user preferences, decomposing complex queries into sub-tasks, and dynamically interacting with heterogeneous online tools and databases (e.g., real estate listings, school ranking data, maps). The goal is to generate recommendations that meet users’ multi-objective constraints. Inspired by recent agentic approaches, this project will explore how autonomous web agents can coordinate and perform sequential web-based operations to solve real-world decision-making problems.

 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.

This project uses machine learning and predictive analytics to group customers based on their shopping habits using publicly available or synthetic transactional datasets. Students will clean and analyse purchase data, apply clustering algorithms such as K-Means and Hierarchical Clustering, and identify common product purchase patterns using association rule mining. The project aims to show how data-driven methods can help businesses better understand customer behaviour and design targeted marketing strategies.

 Title: Grouping Customers by Shopping Habits with Machine Learning