Research projects in Information Technology

Displaying 41 - 50 of 188 projects.

AI models for skin conditions management and diagnosis

Problem:

Almost 1 million people in Australia suffer from a long-term skin condition.  Without early intervention, skin conditions become chronic conditions with significant health, psychosocial and economic impacts, including anxiety, depression and social isolation. Access to safe, timely, high-quality specialist care leads to better outcomes for individuals. With roughly 2 dermatologists per 100,000 Australians, it’s not surprising how hard it is to have access to dermatologist expertise.

Solution:

Supervisor: Dr Yasmeen George

Large language models for detecting misinformation and disinformation

The proliferation of misinformation and disinformation on online platforms has become a critical societal issue. The rapid spread of false information poses significant threats to public discourse, decision-making processes, and even democratic institutions. Large language models (LLMs) have shown tremendous potential in natural language understanding and generation. This research aims to harness the power of LLMs to develop advanced computational methods for the detection and mitigation of misinformation and disinformation. More specific objectives are:

Supervisor: Assoc Prof Thanh Thi Nguyen
A futuristic image of outlines of a group of 14 people as if seen through a scanner.

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.

Supervisor: Prof Patrick Olivier

AI-augmented coaching, reporting and its assessment

This project will develop general cutting edge generative AI and natural language processing methods to advance AI-augmented human-in-the-loop coaching and associated training planning and outcome reporting.

Supervisor: Dr Levin Kuhlmann

Brain network mechanisms underlying anaesthetic-induced loss of consciousness

This project focuses on brain network mechanisms underlying anaesthetic-induced loss of consciousness through the application of simultaneous EEG/MEG and neural inference and network analysis methods. In this work we study the effects putative NMDA antagonists xenon, a potent anaesthetic, and nitrous oxide, a weak anaesthetic, on anesthetic-induced changes in brain mechanisms and networks.
Supervisor: Dr Levin Kuhlmann

Model-based depth of anaesthesia monitoring

This project involves model-based depth of anaesthesia monitoring using autoregressive moving average modelling and neural mass and neural field modelling of the electroencephalographic (EEG) signal. This will be achieved through frequency domain and time domain state and parameter estimation techniques to infer model states and parameters in real time to simultaneously track the anaesthetic brain states while inferring underlying physiological changes.
Supervisor: Dr Levin Kuhlmann

Epileptic Seizure Prediction

Seizure prediction algorithms will be developed using the one-of-a-kind ultra-long-term human intracranial EEG dataset obtained from the Neurovista Corporation clinical trial of their Seizure Advisory System, or data from other implantable or wearable devices. This involves consideration of both feature-based machine learning or data science approaches and neural mass parameter estimation approaches to classify the EEG and predict seizures. Recent approaches focus on critical slowing as a marker for seizure susceptability and the influence of brain rhythms.

Supervisor: Dr Levin Kuhlmann

Securing Generative AI for Digital Trust

Project description: Generative AI models work by training large networks over vast quantities of unstructured data, which may then be specialised as-needed via fine-tuning or prompt engineering. In this project we will explore all aspects of this process with a focus on increasing trust in the model outputs by reducing or eliminating the incidence of bugs and errors.
Supervisor:
A linear relaxation of an integer programming problem

End-to-End Prediction and Optimisation for Neuro-Symbolic Artificial Intelligence

Optimisation methods, such as mixed integer linear programming, have been very successful at decision-making for more than 50 years. Optimisation algorithms support basically every industry behind the scenes and the simplex algorithm is one of the top 10 most influential algorithms. Major success stories include rostering nurses in hospitals, managing chains of organ transplants, planning production levels for manufacturing, routing delivery trucks for transport, scheduling power stations and electricity grids, to name just a few.

Supervisor: Dr Edward Lam
Truck and drone routing and scheduling

Branch-and-Cut-and-Price Algorithms for Computing Cost-Effective and Time-Efficient Delivery Routes for Trucks and Drones

Transport and logistics businesses today use a large fleet of trucks and vans to deliver packages widely across a city. Deciding which package should be loaded on to which vehicle and deciding which package should be prioritised are surprisingly difficult computational tasks. State-of-the-art high-performance algorithms are used to calculate routes for the vehicles in order to minimise costs and maximise efficiency.

Supervisor: Dr Edward Lam