Research projects in Information Technology

Understanding protein structures is fundamental in food science, but traditional 2D representations often fail to convey their complex 3D shapes and interactions. This project aims to develop VR/AR applications that allow users to explore protein structures interactively and immersively, enhancing comprehension of protein function, behavior, and their roles in food systems.

Exciting opportunities to work on a new Discovery Project: Enhancing learner feedback literacy using AI-powered feedback analytics!

Project description:

This scholarship opportunity is open to domestic applicants who identify as Aboriginal or Torres Strait Islander.

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.

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:

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. 

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.

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. 

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. However, these studies often provide dietary advice based on the average responses of groups to specific foods, rather than considering individual glycemic responses.