I'm a researcher and consultant specialising in federated learning and privacy-preserving AI systems. My work sits at the intersection of academic research and real-world deployment — helping organisations unlock the value of machine learning without compromising sensitive data.
My academic background is in cyber security, with a PhD focused specifically on data security. It was through that lens — understanding deeply how data can be exposed, exploited and protected at a technical level — that I found federated learning to be one of the most compelling answers to a problem organisations genuinely struggle with.
Since completing my PhD, I have spent the last five years working exclusively in the federated learning space. Alongside this consultancy, I am an active postdoctoral researcher at UNSW, where my research continues to focus on FL systems — meaning the advice and architectures I bring to clients are directly informed by current academic work in the field, not just past experience.
Doctoral research specialising in data security — providing a deep technical foundation for understanding how sensitive data is exposed, and how systems can be designed to prevent it.
Five years of focused work designing, implementing and evaluating federated learning systems across a range of domains — from system architecture to aggregation protocol design.
Active researcher at the University of New South Wales, one of Australia's leading Group of Eight universities. Current research focuses on federated learning systems, ensuring consultancy work is grounded in the latest developments in the field.
You are not working with a generalist consultancy that has added AI privacy to a service list. You are working directly with a specialist whose entire career has been pointed at this problem — with the academic rigour to back it up.