This Ph.D. project aims to combine causal analysis with deep learning for mental health support. As deep learning is vulnerable to spurious correlations, novel causal discovery and inference methods will be developed to identify and reason over causal relationships among all associations from the data in literature. As the number of causal relationships is usually much smaller than that of associations, the proposed techniques will achieve explainability by making causes and effects interpretable to psychologists.
Research projects in Information Technology
Displaying 31 - 40 of 112 projects.
Bayesian-network models for human-machine collaboration to protect pollinator-plant interactions in agriculture and natural ecosystems
Ecological systems are dynamic and complex. Many ecosystems support human food production and in turn are impacted by human food production activity. This creates feedback loops between ecosystems, human society and our agriculture, that are typical of complex systems. Ecosystem and social system modelling therefore, including simulation, can play a key role to understand food production and ecosystem interactions.
Supervisor: Assoc Prof Alan Dorin
Formal Explainability in Artificial Intelligence
Artificial Intelligence (AI) models are widely used in decision making procedures in many real-world applications across important areas such as finance, healthcare, education, and safety critical systems. The fast growth, practical achievements and the overall success of modern approaches to AI guarantees that machine learning AI approaches will prevail as a generic computing paradigm, and will find an ever growing range of practical applications, many of which will have to do with various aspects of humans' lives including privacy and safety.
Supervisor: Alexey Ignatiev
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
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
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