Honours and Minor Thesis projects

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.

Computational creativity is a subfield of AI that aims at studying theoretical and practical issues of creative behaviour by computational agents. An interesting aspect of creative behaviour is that it involves intrinsic factors such as motivations, intentions, struggle, etc. With this project we are interested in studying such intrinsic factors in a computational setting through the use of the Belief-Desire-Intentions (BDI) model. The project involves carrying out a survey of existing work on the use of the BDI model in AI in general and computational creativity in particular…

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.

Rational Verification is the problem of checking temporal logic properties of multi-agent systems modelled as multi-player games. Typically, in rational verification, we want to check whether a temporal logic formula is, or is not, satisfied on some or all equilibria of the game, assuming non-cooperative behaviour, for instance, as given by the Nash equilibria of the game.

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.…

Reinforcement Learning (RL) systems can be represented as Markov Decision Processes (MDPs), which are graph-based models of probabilistic behaviour. Typically, a logic over MDPs predicates only about temporal or epistemic properties of such systems, but fails to express properties about the learning behaviour that such systems may represent. In this project, the aim is to investigate extensions of temporal and epistemic logics to be able to express learning properties of RL systems consisting of multiple agents.