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Research projects in Information Technology

Displaying 1 - 10 of 38 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

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.

Platforming participatory research data governance

Research data governance is an under-explored issue, and technical infrastructures to support the transparency and control of data collected in human research studies (from medicine to social sciences) focus primarily on the researchers rather than the people whose data has been collected. While data protection legislation worldwide is increasingly regulating what companies can do with their customers' data and providing legal mechanisms for customers to access and control such data, the same cannot be said for data collected in research studies.

Information Visualisation: the design space of experimental methodologies

Empirical studies in Information Visualisation research have become more commonplace in the past two to three decades. While formerly the research focus was primarily on utilising the power of novel technologies for presenting data and information in innovative ways, perspectives have changed over time so that evaluating the worth of visualisations (for user, for task, for context) is now considered a crucial stage of the research process.

Social, Political, Economic Studies of Technology and FIRE (Finance, Insurance, Real Estate)

This research project is part of a DECRA fellowship funded by the Australian Research Council for a project titled, Everyday Insurtech: Impacts of Emerging Technology for Insurance. The fellowship will study the development, adoption, and implications of digital technology and insurance—such as tools for capturing individualised data about behavioural risk factors and automating enforcement of policy conditions.

Supervisor: Dr Jathan Sadowski