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.

This project investigates the technical dimensions of real estate asset tokenization, with a particular focus on the challenges of integrating blockchain technology into the real estate sector. While tokenization promises to enhance liquidity, efficiency, and transparency in property transactions, its practical implementation faces significant technological hurdles. The project will examine key issues such as data interoperability, smart contract design, secure digital identity management, and scalability of blockchain networks in handling complex real estate assets.

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.

Time series are an ever growing form of data, generated by numerous types of sensors and automated processes. However, machine learning and deep learning methods for analysing time series are much less advanced than for other forms of data.

Our research is revolutionising the analysis of time series data. But it is early days, and many more impactful challenges are yet to be overcome.

This project is funded by the Australian Research Council and will be conducted as part of a large world-leading research team.

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.