I am a cancer data scientist working at the intersection of deep learning and large-scale molecular biology. I read Computer Science at Monash University, graduating top of my cohort, then spent several years building electronic-trading systems at Goldman Sachs before moving into cancer research. I completed my PhD in Cancer Data Science at the University of Sydney and the Children's Medical Research Institute (CMRI), where I am now a Cancer Institute NSW Early Career Fellow and Senior Data Scientist; I also hold an Adjunct Lecturer appointment at the University of Sydney.

My research develops federated, generative, and transformer-based deep-learning methods for cancer multi-omics. As co-first author of the Cancer Cell pan-cancer proteomic map of 949 human cancer cell lines, I helped build one of the field's most widely used resources. My first-author publications introduced federated deep learning for privacy-preserving cancer subtyping — the first application of federated deep learning to cancer proteomics, spanning some 7,500 proteomes (Cancer Discovery) — and MOSA, a generative-AI approach to synthetic augmentation of multi-omic datasets (Nature Communications). 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 as sole chief investigator, supporting a three-year programme on proteomic foundation models integrated with federated learning for multi-hospital cancer research. Throughout, my goal is to translate advanced computational methods into practical tools that improve cancer diagnosis and prognosis through precision oncology.