Research projects in Information Technology

Recent advances in artificial intelligence have produced highly accurate diagnostic models across a wide range of medical applications. However, these systems often provide little insight into how decisions are made, limiting clinician confidence and adoption in healthcare settings.

Graph learning has become one of the most successful approaches for analysing complex biomedical data such as brain connectivity networks, molecular interactions, and patient similarity graphs. However, most existing graph neural networks are developed for individual diseases or specific datasets, limiting their ability to generalise across different clinical applications.

Artificial Intelligence is rapidly transforming healthcare by assisting clinicians in disease diagnosis, prognosis, and treatment planning. While recent advances in deep learning and large language models (LLMs) have significantly improved predictive performance, most existing AI systems remain passive prediction tools that lack transparency, reasoning capability, and reliability. These limitations hinder their adoption in real-world clinical practice, where explainability, trust, and accountability are essential.

Please note that this PhD topic is offered exclusively at our Monash Malaysia campus and is not available at the Clayton campus.

Core PhD Question

How can we prevent AI systems, advertisers, platforms, and malicious actors from constructing psychological profiles of social media users while still allowing meaningful online interaction, personalization, and content discovery?

Please note that this PhD topic is offered exclusively at our Monash Malaysia campus and is not available at the Clayton campus.

Core PhD Question

How can we design autonomous AI security systems that can detect, reason about, respond to, and recover from cyberattacks with minimal human intervention, while remaining safe, explainable, and resistant to manipulation?

Please note that this PhD topic is offered exclusively at our Monash Malaysia campus and is not available at the Clayton campus.

Core PhD Question

How can we design future medical AI systems that remain secure, privacy-preserving, explainable, and clinically reliable when exposed to adversarial attacks, prompt injection, poisoned data, privacy leakage, and unsafe autonomous AI-agent behaviour?

Please note that this PhD topic is offered exclusively at our Monash Malaysia campus and is not available at the Clayton campus.

Core PhD Question

Can Transformer models understand the full lifecycle of a vulnerability; from vulnerable code, to patch, to advisory, to regression risk; and determine whether a security fix is complete, safe, and trustworthy?

So we are not planning to do the following:

Medical VLMs - especially if they can be deployed inside "corporate firewalls" or under the direct governance of health services - potentially offer great advantages over general purpose VLMs for a range of reasons.

However there are fundamental questions over their performance and suitability for deployment inside healthcare, and as to whether they can compete given the mega-infrastructure and ability to upgrade that is available to the big players (OpenAI, Anthropic etc). 

The rapid deployment of increasingly capable AI agents has prompted a fundamental reassessment of how safety should be built into AI systems. Bengio and colleagues have argued that purely agentic training objectives are intrinsically risky and have proposed an alternative paradigm: a non-agentic "Scientist AI" that explains the world from observations rather than acting in it, combining a world model that generates explanatory theories with a question-answering inference machine, and operating with explicit notions of uncertainty so as to mitigate overconfident predictions [1].

Several explanations for hallucination exist, but perhaps the main reason is that they are trained to be plausible.  There is no element of truth-seeking in their construction.  The training content can be filtered to support this, but in many areas truth is not established.   Many text sources are opinions, may contain argumentation and indeed subtle or not so subtle propaganda, written in many dfferent styles, and some reflect misinformed views.   Even Wikipedia sources are known to have bias (so called "establishment bias").