My research sits at the intersection of computer vision, deep learning and applied data science, with a current focus on maritime surveillance. As a Postdoctoral Researcher at Adelaide University, I develop and deploy object-detection and multi-object tracking pipelines — combining modern architectures such as YOLO and LSTMs with classical computer vision and tracking frameworks like Stone Soup — to analyse UAV imagery and reconstruct vessel trajectories from noisy, real-world data.

This work builds on a decade spanning fundamental research and industry. I completed my PhD in experimental high-energy physics as a member of the ATLAS collaboration at CERN, where I analysed daily terabytes of collision data and developed detector-calibration methods still used across the experiment. Since then I have applied machine learning, generative AI and large-scale data engineering across domains including aquaculture forecasting, natural-language generation, and utilities and aged-care analytics. My broader interests include computer vision, generative and language models, statistical modelling, and turning complex, high-volume data into decisions.