Honours and Minor Thesis projects

Since the 1990s, researchers have known that commonly-used public-key cryptosystems (such as RSA and Diffie-Hellman systems) could be potentially broken using efficient algorithms running on a special type of computer based on the principles of quantum mechanics, known as a quantum computer. Due to significant recent advances in quantum computing technology, this threat may become a practical reality in the coming years. To mitigate against this threat, new `post-quantum’ (a.k.a.

As a pregnancy approaches term (the point at which the foetus is considered fully developed), decisions are made about the timing of birth and the way babies are born. These decisions are incredibly challenging for clinicians and pregnant women. Digital health records, advances in big data, machine learning and artificial intelligence methodologies, and novel data visualisation capabilities have opened up opportunities for a dynamic, ‘Learning Health System’ – where data can be harnessed to inform real-time and personalised decision-making.…

People who are blind need to touch surfaces and materials to get information. These surfaces can be a Braille paper that has Braille text, a swell paper that has embossed shapes, and a button that is used to turn on and off a device like a TV or to open a train carriage door.

Despite the rapid progress made recently, deep learning (DL) approaches are data-hungry. To achieve their optimum performance, a significantly large amount of labeled data is required. Very often, unlabelled data is abundant but acquiring their labels is costly and difficult. Many domains require a specialist to annotate the data samples, for instance, the medical domain. Data dependency has become one of the limiting factors to applying deep learning in many real-world scenarios.

Graph neural networks (GNNs) are widely used in many applications. Their training graph data and the model itself are considered sensitive and face growing privacy threats.
 

Modern machine learning is increasingly applied to create amazing new technologies and user experiences, many of which involve training machines to learn responsibly from sensitive data, such as personal photos or email. Ideally, the parameters of trained machine-learning models should encode general patterns rather than facts about specific training examples.

Machine learning (ML) training and evaluation usually involve large-scale datasets and complicated computation. To process data efficiently, a promising solution is to outsource the processes to cloud platforms. However, traditional approaches of collecting users' data at cloud platforms are vulnerable to data breaches.

Disruptive technologies such as artificial Intelligence (AI) systems can have unintended negative social and business consequences if not implemented with care. Specifically, faulty or biased AI applications may harm individuals, risk compliance and governance breaches, and damage to the corporate brand.

Issues and solutions exist on different aspects of the management of real-time data, such as persistence, visualisation, and online processing. This project is a research project to identify the significant issues of real-time data management in structural health monitoring (SHM), particularly for bridges, and implement an integrated software solution for enterprise usage. This project involves time series database design, visualisation and online processing of time series, and service-oriented and web-based software development.

Traditionally many organisations prefer to work with text-based reports based on quantitative data collection.

These reports are used to share their insights and practices within their organisation, with other organisations, with donors and with community members.

In recent years, community generated qualitative data, in the form of audio or video content is more widely becoming a mode of data collection for organisations.