Honours and Masters project

Leveraging the FLoRA adaptive learning platform, we will conduct a five-phase research program combining experimental studies and advanced trace data analysis. Through time-stamped interaction data, we aim to detect behavioural signals of metacognitive disengagement using machine learning and time-series modeling techniques. These insights will inform the development of adaptive scaffolding tools that encourage students to monitor, evaluate, and adjust their learning strategies when using GenAI.

This project aims to analyse the comments of Twitter  on non-communicable diseases.  Students are expected to carry out Aspects Detection to identify the specific aspects discussed in the tweets e.g., causes, transmission and symptoms. Subsequently,  students are expected to conduct sentiment analysis utilizing tools like TextBlob or VADER, while also taking into account the importance of considering emojis to enhance classification accuracy.

Deep learning has achieved ground-breaking performance in many 2D vision tasks in the recent years. With more and more 3D data available such as those captured by Lidar, the next research trend is doing advanced perception on 3D data. The objective of this project is to study the state-of-the-art object detection techniques for 3D point clouds such as PointNet and PointVoxel.

3D localisation, reconstruction and mapping of the objects and human body in dynamic environments are important steps towards high-level 3D scene understanding, which has many applications in autonomous driving, robotics interaction and navigation. This project focuses on creating the scene representation in 3D which gives a complete scene understanding i.e pose, shape and size of different scene elements (humans and objects) and their spatio-temporal relationship.

Within the faculty's Centre for Organisational and Community Informatics, the Archives and the Rights of the Child Research Program is investigating ways to re-imagine recordkeeping systems in support of responsive and accountable child-centred and family focused out-of-home care. Progressive child protection practice recognises the need, where possible, to support and strengthen parental engagement in the system in order to ensure the best interests of the child. 'No single strategy is of itself effective in protecting children.

The last two decades have witnessed a sharp rise in the amount of data available to business, government and science. Data visualisations play a crucial role in exploring and understanding this data. They provide an initial grasp of the data and allow the assessment of findings of data analytics techniques. This reliance on visualisations creates a severe accessibility issue
for blind people (by whom we mean people who cannot use graphics even when magnified).

To operate, interact and navigate safely in dynamic human environments, an autonomous agent, e.g. a mobile social robot, must be equipped with a reliable perception system, which is not only able to understand the static environment around it, but also perceive and predict intricate human behaviours in this environment while considering their physical and social decorum and interactions.

With up to 1 in 9 Australians affected and an incidence on the rise, there is a clear need to understand the mechanisms driving asthma. This research project aims to dig deep into the early origins of this disease using cutting-edge sequencing technologies in order to identify targets that could be the focus of new therapies and prevention strategies. Historically, studies have focused on one specific aspect of the disease; for example genetics and heritability, environmental factors, microbiome, or respiratory infections.…

In this project, you will build an autonomous agent in the MineRL environment for playing Minecraft or an agent for Animal-AI.  Herein, you will learn how to incorporate symbolic prior knowledge for improving the performance of an agent trained by using deep reinforcement learning (RL) technique, which is the core technique to build AlphaGo.

Current studies on diabetes recommender systems and apps mainly focus on the performance and personalisation of AI models and techniques, including machine learning and deep learning models that are trained on user data. These works often use a one-size-fits-all approach for presenting information to users. Yet, research shows that humans process information in different ways, and their attitudes towards an action depend on their attitude-function styles.