Academic Lecturer
University of Notre Dame Australia
Lectures, tutorials, practical sessions, assessment and student support across Cyber Threat Intelligence and Network Security & Cryptography.
I am a final-stage PhD researcher in Computer Science at Murdoch University, working on machine unlearning, multimodal AI and trustworthy vision-language systems.
I’m particularly interested in selective forgetting, model auditing, recovery attacks and reliable evaluation. My work asks a simple question: when a model appears to forget, what evidence shows that the information is actually gone?
Alongside the PhD, I teach university courses across artificial intelligence, intelligent systems, cyber threat intelligence, network security and cryptography.
I also build reproducible research software, interactive teaching tools and applied AI systems that make technical ideas easier to inspect and use.
A snapshot of the research portfolio as it stands now: thesis progress, accepted work and the questions I am actively pushing forward.
Machine unlearning · multimodal / VLM systems
Three parts of my research you can open directly rather than only read about. Each connects to code, experiments or a research artifact.
Hessian-guided refinement for selective unlearning without paying the cost of full retraining.
Boundary deformation and projected-gradient control for targeted multimodal forgetting.
DurableUn tests whether quantisation can recover behaviour that appeared to be forgotten.
Public research engineering across experiments, repositories, teaching builds and paper artifacts.
Research, teaching and technical delivery across Australian universities.
Lectures, tutorials, practical sessions, assessment and student support across Cyber Threat Intelligence and Network Security & Cryptography.
Teaching across Artificial Intelligence, Intelligent Systems and Transition to IT for undergraduate and postgraduate cohorts.
Machine unlearning, multimodal AI, privacy-preserving ML, model auditing, membership inference, recovery attacks and reproducible experimental pipelines.
Selected first-author research and current doctoral portfolio.
Computer Science · Machine Unlearning · Thesis finalisation
Computer Science
Machine unlearning research, multimodal AI and the software engineering that connects them.
Designing and auditing methods that remove targeted behaviour while protecting retained capability.
Vision-language systems, representation analysis and trustworthy multimodal model evaluation.
Building the tooling around experiments and turning research ideas into reproducible systems.