Honours and Masters project

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

1. Background

Skin cancer, including melanoma, basal cell carcinoma (BCC), and squamous cell carcinoma (SCC), is a significant health concern in Australia. Early detection and accurate risk assessment can support timely clinical assessment and treatment.

2. Research Aim

This research aims to develop an AI and machine-learning framework for early detection, risk classification, clinical decision support, and forecasting of skin cancer.

3. Data and Methods

Lung cancer prediction research in computer science focuses on overcoming critical data engineering and algorithmic challenges—such as severe class imbalance in Electronic Health Records (EHR), high-dimensional tabular feature interactions, and the opacity of deep neural networks.

To address these AI-specific bottlenecks, this research proposes an end-to-end Predictive Artificial Intelligence pipeline designed for early risk scoring and multi-class stage prediction.

1. Research Objectives & Predictive AI Architecture

Brain cancer—particularly High-Grade Gliomas (HGG) like Glioblastoma—remains one of the most lethal central nervous system malignancies, primarily due to non-specific early symptoms, rapid infiltrative growth, and late-stage clinical detection. Standard diagnostic pathways rely heavily on invasive biopsies and complex neuroimaging (e.g., MRI), which can delay intervention and lead to poor overall survival outcomes.