Profile
I am a near-completion PhD candidate in computer science and engineering at Swinburne University of Technology, with a strong academic and practical background in deep learning, computer vision, and system-level development. My doctoral research focuses on medical image segmentation and analysis, addressing key challenges such as data scarcity and inter-expert variability through the development of methods based on self-supervised learning, multi-task learning, and federated learning. This work involved designing and implementing scalable end-to-end frameworks for data processing, model training, evaluation, and deployment
In parallel, I have worked closely with clinicians at Peter MacCallum Cancer Centre, translating research outcomes into clinically relevant systems for automated medical image analysis and structured reporting. This experience strengthened my ability to bridge methodological innovation with real-world application, ensuring that developed solutions are both technically sound and practically deployable.
Beyond my PhD, I have developed a broad engineering skill set through projects spanning traffic incident monitoring systems, satellite signal simulation, and robotics. These experiences demonstrate my ability to rapidly adapt to new domains, build complete systems from the ground up, and collaborate effectively in interdisciplinary teams.
Overall, my background combines rigorous research capability with strong programming and system design skills, with a focus on delivering solutions that have tangible real-world impact.
