Displaying 1 - 10 of 209 projects.
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").
In EdgeVLMOpt (EVO): Optimizing Vision-Language Models for Resource-Constrained Edge Devices, we aim to develop efficient and scalable techniques to enable the deployment of advanced vision-language models (VLMs) on edge hardware. While VLMs have demonstrated strong capabilities in multimodal reasoning and understanding, their high computational and memory demands pose significant challenges for real-time, on-device applications.
In EdgeFusionAI (EFAI): Real-Time Multi-Sensor Multi-Modal Intelligence on Edge Devices, we aim to design and develop efficient techniques for fusing heterogeneous sensory data, including vision, LiDAR, radar, and other modalities, to enable robust and real-time decision-making on resource-constrained edge platforms. This project focuses on building intelligent systems capable of integrating diverse data sources while addressing the challenges of limited computation, memory, and energy availability at the edge.
This project involves the automated generation of textual descriptions for audio content, such as spoken language, sound events, or music. This process typically employs deep learning techniques, such as recurrent neural networks, transformer models, and so on, to analyse audio signals and generate coherent captions. By training on large datasets that include both audio recordings and corresponding textual descriptions, these models learn to recognize patterns and contextual meanings within the audio.