Honours and Masters project

This project will investigate counter-unmanned aerial system (C-UAS) technologies for the detection and mitigation of malicious drones. With the increasing accessibility of small UAVs, there is a growing need for effective technical solutions to identify and neutralize unauthorized aerial threats. The project will explore a broad range of C-UAS methods, including but not limited to networking-based detection and coordination techniques, machine learning, and both active and passive mitigation approaches.

Multi-user virtual reality (VR) systems are becoming more common as these head-mounted displays (e.g., Oculus Quest, HTC Vive) become less expensive and thus more widely available. It is now feasible to have multiple users sharing a co-located VR space, and objects in the space. We are interested in developing cross-platform multi-user VR systems, supporting hardware ranging from desktop computers to high-end head-mounted displays (Quest 3), to low-fidelity devices such as Google Cardboard. The latter introduces complex challenges in supporting user interaction in shared VR environments.…

Multi-user virtual reality (VR) systems are becoming more common as these head-mounted displays (e.g., Oculus Quest, HTC Vive) become less expensive and thus more widely available. It is now feasible to have multiple users sharing a co-located VR space, and objects in the space. Traditionally, VR systems tend not to support the sense of touch, but recent advances in haptics (devices simulating physical objects) now make this possible.

Cybersickness (nausea, disorientation) due to exposure to virtual environments has long been a problem in virtual reality (VR) and has been shown to reduce the effectiveness of VR environments. It usually occurs due to mismatches between visual and vestibular (motion) cues, for example, when moving through a 3D environment using a joystick, which does not yield correct motion cues. There are several approaches to reducing cybersickness in VR, most notably, reducing the field of view ("tunneling") during motion, or discrete motion ("snapping" movement and rotation).

In this project, we will conduct an empirical study to understand certain problems in software systems. 

In this project, students and me will work together to develop a new technique for software testing and debugging. 

The subject under test may be AI/LLM. The technique may involve AI/LLM as well. 

Network visualisation (or 'graph drawing') algorithms allow us to see connections and patterns in a network clearly. There are a large number of such algorithms that depict ('lay out') networks 'nicely' - according to a set of well-established criteria. These criteria apply to neat diagrams - with clear straight (nor neatly bent or curved) lines ('edges').

This project will investigate how networks that have been sketched (with curved/ wavy lines) can be adapted so that they, too, can be assessed by typical graph layout criteria.

Poisson regression is a fundamental tool for modeling count data, appearing ubiquitously in applications ranging from epidemiology (disease counts) and ecology (species abundance) to economics (patent counts) and social sciences (event frequencies). The classical generalized linear model (GLM) framework treats count outcomes as Poisson-distributed with a log-linear relationship to covariates.

Decision trees are powerful, interpretable models for prediction and classification that recursively partition the feature space into regions with homogeneous outcomes. Traditional decision tree algorithms like CART and C4.5 rely on heuristic splitting criteria and require ad-hoc pruning methods to prevent overfitting. In contrast, the Minimum Message Length (MML) framework provides a principled, information-theoretic approach to tree induction that naturally balances model complexity against data fit without requiring separate pruning phases.

Minimum Message Length (MML) is an elegant information-theoretic framework for statistical inference and model selection developed by Chris Wallace and colleagues. The fundamental insight of MML is that both parameter estimation and model selection can be interpreted as problems of data compression. The principle is simple: if we can compress data, we have learned something about its underlying structure.