Honours and Masters project

Studies using computed tomography (CT), particularly high-resolution CT and quantitative CT have become a crucial non-invasive method for examining airway thickness and the structural changes known as airway remodeling in chronic respiratory diseases such as asthma. Bronchial thermoplasty is a treatment option for patients with severe asthma and works by applying heat energy to reduce the amount of excess airway smooth muscle, which is often abnormally thickened in patients with asthma.

Modern transport and logistics rely on efficient routing to ensure that goods are delivered on time and at minimal cost. Determining the optimal order in which vehicles visit a set of customers is a fundamental challenge in mathematics and computer science. Despite decades of research, this problem remains computationally difficult. To achieve reasonable cost-effectiveness in industry-scale applications, the best-performing methods rely on heuristic and suboptimal solvers. This project will develop new suboptimal algorithms for tackling large instances relevant to practice.

Shortest path algorithms are used to advise travelers on the best route from an origin to destination across various modes including car and public transport. One important variant of the standard shortest path problem is the inclusion of resource constraints, where a user has a limited budget in cost, time, carrying capacity, etc. and needs to find a shortest path within this budget. Unlike the regular shortest path problem, the resource-constrained shortest path problem is theoretically proven to be NP-hard, making its computation difficult.

We explore novel map representations and projections.

This project seeks to explore and trial new map representations for seeing Australian population data sets more effectively.

Whilst Australia has a population of 28 million, nationally it is counted as of the least densely populated in the world due to its size and topography (large areas of semi-arid and desert geography). Yet Australia is one of the world's most urbanised countries with 89% of the population living in a handful of urban areas, mostly with 50KM of the coastline along the east and south coastline. 

An "agent" or "client" in the context of mobile forensics is the program deployed using an exploit to a mobile device to facilitate extraction of the data from the phone. Some agents can also do other functions like passcode brute-force attacks.

In today's digital landscape, cyberattacks are increasingly impacting organisations by disrupting critical services and compromising sensitive data. As these attacks grow in volume and complexity, security teams are increasingly challenged to safeguard sensitive data and maintain operational continuity. Manual efforts of security assessment often led to inconsistent and delayed results, high operational costs, and increased window of opportunity for potential attackers. To effectively mitigate these risks, there is a pressing need for automated security assessment.

Providing timely, individualised feedback is a persistent challenge in large-scale computing units. This project investigates how Generative AI models can automatically produce pedagogically aligned, rubric-based feedback on student submissions. A prototype system will interface with an LLM API (e.g., OpenAI GPT) and generate structured feedback, which will be evaluated for accuracy, usefulness, and tone against educator benchmarks.

Hybrid quantum-classical algorithms such as the Quantum Approximate Optimisation Algorithm (QAOA) and Variational Quantum Eigensolver (VQE) integrate quantum circuits with classical optimisers to solve hard optimisation problems. This project will develop a small-scale hybrid framework in Qiskit to tackle problems like job-shop scheduling or micro-grid energy dispatch, comparing quantum and classical approaches in accuracy and computational efficiency.

Quantum algorithms such as Grover’s Search promise quadratic speed-ups over classical search but are sensitive to hardware noise. This project will use Qiskit Aer to model realistic noise channels (decoherence, gate and readout errors) and evaluate their impact on algorithmic performance. By varying circuit depth, qubit count, and noise parameters, the student will identify conditions under which quantum advantage remains achievable and investigate possible error-mitigation strategies.

Optimal policies derived from decision-theoretic models such as Markov Decision Processes (MDPs) often prescribe a single “best” action for every state. However, in real-world conservation contexts, managers rarely follow these prescriptions perfectly—due to uncertainty, limited trust, or operational constraints. This project explores how to make optimal policies more useful and interpretable by helping managers identify which states are critical to get right.