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.
Honours and Masters project
Displaying 1 - 10 of 286 honours projects.
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.
Pupil Labs eye tracking for visualisation experimentation
This is a Winter Student Research Internship 2026 advert (and already filled).
However it will convert to honours /minor thesis project after the break. If you are interested in this research as a thesis particularly the 3D component, please contact me.
(Note that Winter and summer student internships must be applied for here:
https://www.monash.edu/study/fees-scholarships/scholarships/summer-winter)
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