Future electricity networks are becoming increasingly complex due to the rapid growth of distributed energy resources (DERs), batteries, renewable generation and intelligent infrastructure. Although modern networks collect large volumes of operational, asset and maintenance data, this information is often distributed across multiple systems, making it difficult for engineers and operators to understand the true reliability and resilience of the network or identify emerging risks before failures occur.
Honours and Masters project
Displaying 1 - 10 of 285 honours projects.
Explainable epidemiology
A lightweight, explainable “Epi-Copilot” that combines generative AI, biostatistics, and epidemiology to turn routinely collected, formatted primary care data (and related signals such as immunisation records, diagnoses, pathology trends, and medication changes) into actionable, interpretable risk insights for clinicians and epidemiologists. The goal is not a black-box predictor, but an explainable epidemiology system that supports proactive care and safety surveillance.
Deep Self-Supervised Learning of Bayesian Network Structures through Graph and Data Masking
Bayesian Networks (BNs) are widely used for modelling uncertainty and causal relationships in domains such as healthcare, finance, cyber security and decision support. However, learning the optimal BN structure directly from observational data remains computationally challenging due to the super-exponential search space of possible graphs.
Quantum computing approach for Bayesian network inference under realistic assumptions
Bayesian network (BN) inference—computing posterior probabilities given evidence—is a core task in probabilistic reasoning, but it becomes computationally expensive as networks grow in size or treewidth increases. Quantum-accelerated BN inference explores whether quantum algorithms and quantum circuit representations can provide practical advantages for approximate inference and sampling, while still making realistic assumptions about data access, noise, and limited quantum resources.
Quantum computing approach for Bayesian network structure learning
Quantum-accelerated Bayesian network (BN) structure learning asks whether quantum algorithms can speed up the combinatorial search over directed acyclic graphs while still making realistic systems assumptions.
Developing a Feedback Literacy Maturity Model from Unit-Level Feedback Data
Feedback is central to student learning, but feedback does not automatically lead to improvement. Students need opportunities to understand, evaluate, and act on feedback, while teachers and teaching teams need to design feedback practices that are clear, actionable, timely, and connected to learning activities.
AI and Data Science for Saving our Australian Wildlife
Background and motivation
A Data-Centric Study of Dataset Quality for TTP Extraction
Cyber Threat Intelligence (CTI) plays a vital role in today's cybersecurity landscape by collecting and analysing data about current and potential threats, providing insights to better understand, mitigate and respond in this ever-evolving environment. A core component of CTI is the identification of adversarial Tactics, Techniques, and Procedures (TTPs), which describe how attackers operate at a strategic and operational level.