Honours and Minor Thesis projects

Blockchain technology and its popular cryptocurrencies such as bitcoin and Ethereum have most revolutionary technological advances in recent history, capable of transforming businesses, government, and social interactions. However, there is a darker side to this technology which is the immense energy consumption and potential climate impact of the blockchain and cryptocurrencies.

Cloud Data centres are designed to support the business requirements of cloud clients. However, due to the complexities of data centre infrastructure and their software systems, cloud service providers often do not have access to quality data regarding their IT equipment. This hinders their ability to better optimise the quality of their services and system performance. A clear message from across the industry is that better data allows for better decision making and resource management.

In recent years, the production and sales of Electric Vehicles (EVs) have been known as an important growth worldwide. This evolution is mainly due to the severe limits regarding greenhouse gas emission that cannot be respected by internal combustion vehicles. The expected growth in EV adoption creates large opportunities for grid integration, through flexible smart charging and vehicle to grid (V2G) or vehicle to premises (V2P) to moderate peak demand.

Text classification has extensive uses and deep learning has improved its performance using transformer language models.  A major hurdle for its use, however, is the paucity of labelled or annotated data. The data labelling process performed by domain experts is expensive and tedious to produce.  Active Learning is an approach to speeding up learning by judiciously selecting data to be annotated. Recently, advances in active learning theory have been made, but some experimental anomalies occur which need investigating.

Multi-label classification (MLC), which simultaneously assigns several labels to each instance, is critical in a wide variety of domains. One of the most difficult is a subset of data categorisation in which classes are arranged hierarchically and objects can be allocated to many paths of the class hierarchy concurrently. This is referred to as hierarchical multi-label classification (HMC), and it is useful for text classification. For example, the output of a news article may cover a variety of topics, including news, finance, and sports.

Pathfinding is fundamental operation in video game AI: virtual characters need to move from location A to location B in order to explore their environment, gather resources or otherwise coordinate themselves in the course of play. Though simple in principle such problems are surprisingly challenging for game developers: paths should be short and appear realistic but they must be computed very quickly, usually with limited CPU resources and using only small amounts of memory.

This project is part of a larger project on Hand Hygiene, which aims to address hand hygiene in hospitals by combining a suite of digital monitoring products with advanced data analytics. We expect to create a system that monitors the full user journey and performs constant data analysis through a cloud-based system.

This project is part of a larger project on Hand Hygiene, which aims to address hand hygiene in hospitals by combining a suite of digital monitoring products with advanced data analytics. We expect to create a system that monitors the full user journey and performs constant data analysis through a cloud-based system.

Although we normally use our fingers to manipulate apps in the smart phone, that operation may not apply in some special situation e.g., checking information when holding your kids, driving, people with disability, etc. In those special scenarios, controlling the app with voice will make it much more convenient. In this study, we are trying to develop a tool to automatically map your voice to operations in mobile apps. You will work with one of my PhD students and me to deliver this exciting work which can be both published in academic and applied into the practice.

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.…