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Research projects in Information Technology

Displaying 1 - 10 of 187 projects.


An AI analytics workbench for protein structural characterisation

Our industry partners are developing software for automation of Hydrogen Deuterium Mass Spectrometry, which can connect structure, behaviour and function of proteins, for understanding diseases and developing drug and vaccine treatments.

Modern AI techniques can provide powerful models for classifying and understanding protein structures, but expert supervision is required in the development, training and deployment of these models into automation scenarios.

Supervisor: Prof Tim Dwyer

XR-OR: Extended Reality Analytics for Smart Operating Rooms and Augmented Surgery

We seek to explore opportunities and challenges for the use of Extended Reality (XR) technologies (including augmented and virtual reality, as well as mixed-reality interaction techniques) to support surgeons, operating room technicians, and other professionals in and around operating room activities. Particular areas that may be explored are:

Supervisor: Prof Tim Dwyer

Visualisation Technology for Cultural Heritage Preservation

New data capture and visualisation techniques present exciting opportunities for advancing cultural heritage communication, preservation, and interpretation. These PhD projects aim to investigate and develop novel visualisation approaches for cultural heritage.
Supervisor: Dr Kadek Satriadi

Immersive Contextual Data Analytics

This PhD project aims to leverage innovative spatial computing technologies and proposes Immersive Contextual Data Analytics (ICDA) as a method to address contextual analysis challenges by bringing rich contextual information to the analyst’s workspace. Despite the technological capability to support ICDA, there remains a lack of fundamental human-computer interaction research and usability design principles to realise practical and effective applications, particularly concerning how data visual analytics translates to this new method.
Supervisor: Dr Kadek Satriadi

Guidelines and Rubrics for developing mobile sensing apps in health care

Mobile and continuous health monitoring has seen major advancements in recent years. The capabilities of current mobile phones and their built-in sensors have inspired many mobile sensing applications for monitoring individuals' health, activities and social behaviour. Yet, there is a lack of common and standard guidelines in developing mobile sensing apps (from both software development and UI perspectives) and their evaluation. 

A multi-layer architecture (the mobile-edge-cloud continuum) of federated learning for mobile health sensing data

Current federated learning architectures in mobile healthcare are limited to a centralised model without considering the full continuum of mobile-edge-cloud. Additionally, to support different data privacy needs of patients as well as the limitations of mobile environments, there is a need for considering a multi-level federated learning architecture for the mobile-edge-cloud continuum.

An online assessment framework for reliable generative AI-driven recommender apps in chronic disease management

Chronic conditions are becoming a serious global and national health problem. Recommendation systems play an important role in supporting patients in managing their long-term health issues. They generally rely on expert rules or machine learning models to provide health advice. Recently, generative AI tools, such as ChatGPT, have become a popular focus of research. In healthcare, they show strong potential to facilitate the process of generating health-related advice without the need for predefined rules or training data. Yet, their reliability remains a serious concern. 

AI-driven mobile recommendation systems for diabetes management

Diabetes can be effectively controlled by maintaining a healthy diet, well-managed blood glucose level and regular physical activity. Evidence suggests that improving dietary habits can play a crucial role in preventing the onset or progression of diabetes. A large number of mobile apps have been recently introduced to assist individuals with self-management of diabetes.

Efficient and Interpretable Modular End-to-end Autonomous Driving System

The future of autonomous driving systems holds great promise, offering a solution to address the challenges associated with human errors and the mental fatigue of driving. However, there are trade-offs between the modularity (henceforth interpretability) and the efficiency in existing end-to-end modular autonomous driving models. In this PhD project, student is expected to conduct research in the area of end-to-end modular autonomous driving using computer vison and deep learning methods.

Supervisor: Dr Loo Junn Yong

Investigating Security and Privacy Issues in Real-World Asset Tokenization

This project will explore the security and privacy challenges inherent in the tokenization of real-world assets (RWAs) using the blockchain technology. As industries increasingly adopt tokenization to digitize and trade assets like real estate, commodities, and fine art, ensuring the security and privacy of these transactions becomes critical. The research will focus on identifying vulnerabilities in existing tokenization frameworks, analyzing potential risks, and developing novel security protocols to protect sensitive data and ensure the integrity of tokenized assets.

Supervisor: Dr Hui Cui