I am a cancer data scientist specialising in artificial intelligence and machine learning for large-scale molecular data analysis. I hold a first-class honours degree in Computer Science from Monash University, where I graduated as the top student, and a PhD in Cancer Data Science from the University of Sydney. After gaining software engineering experience at Goldman Sachs, I transitioned to cancer research and now serve as an Adjunct Lecturer at the University of Sydney and Senior Data Scientist at the Children’s Medical Research Institute (CMRI).

My research focuses on developing and applying deep learning methods to cancer multi-omics data, with particular expertise in federated learning, generative models, and transformer architectures. As co-first author on the landmark Cancer Cell study mapping the proteomic landscape of 949 human cancer cell lines, I contributed to one of the field’s most widely used resources (230+ citations). My subsequent first-author publications in Cancer Discovery and Nature Communications have introduced novel approaches in federated deep learning for privacy-preserving cancer subtyping, and generative AI for synthetic augmentation of multi-omic datasets. I maintain active international collaborations with the Wellcome Sanger Institute (UK) and the University of Lisbon (Portugal).

In 2025, I was awarded a Cancer Institute NSW Early Career Fellowship to develop proteomic foundation models integrated with federated learning for multi-hospital cancer research. My work to date has generated over 900 citations (h-index: 8) and I have contributed to more than $1.59 million in competitive research funding. I am driven by the goal of translating advanced computational methods into practical tools that improve cancer diagnosis and prognosis through precision medicine.