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Research projects in Information Technology

Displaying 1 - 10 of 186 projects.


Causal Reasoning for Mental Health Support

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.

Supervisor: Dr Lizhen Qu

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.

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:

Detecting Threats in Temporal Networks

A PhD is available for an Australian citizen with our industry partner The AiLECS Lab. The project is Detecting Threats in Temporal Networks. The project aims to better understand communications in networks of criminal activity.

This will be led by OPTIMA AI Dr John Betts and OPTIMA CI Prof. Peter Stuckey at Monash University, and Dr Janis Dalins and Dr Campbell Wilson at The AiLECS Lab.

Brief description of the project:

Supervisor: John Betts

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.

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