Welcome! I'm a Sydney-based machine learning (ML) engineer building AI systems with a current focus on the agentic web (the emerging layer of the internet for both humans and AI). I also build AI/ML for digital health apps that connect medical documents, wearable devices, patient logs, and more into unified, patient-centric experiences. I completed my PhD in Computer Science at Aalto University in Finland and I hold a First-Class Honours degree in Applied Mathematics from the University of Sydney. My doctoral research focused on probabilistic machine learning and deep learning for health and genetics.
A practical guide for students and early-career researchers curious about getting started or mastering their use of AI tools in scientific research. Introduces emerging best practices for working with AI in 2026. Includes examples from quantitative biology but broadly applies to any scientific field.
The lives of early-onset cancer patients (ages 20-49) are catastrophically disrupted by diagnosis and treatment. This research presents an app that pairs consumer wearables with multimodal foundation models to unify fragmented clinical data, physiological signals, and the reality of daily life with cancer.
In my PhD I developed new algorithms for large-scale health and genetics data, validated on biobanks and national health registries across Europe. My publications span high-dimensional synthetic data for genetics research and deep learning models for electronic health records — including a hybrid graph-recurrent architecture for modelling disease risk in families.
March 2024
Interview by the Finnish Center for AI about my translational research on AI-powered personalised medicine. My work included generating high-fidelity synthetic data for genetics research, used by the INTERVENE consortium for benchmarking and developing methods for genetic risk scoring.
September 2020
Featured in Sifted (Financial Times) for AI-powered digital health tools to close the data gap in women's health with a digital twin of a patient's health and lifestyle. At Velmio I launched an AI digital health product used by over 30,000 people and deployed machine learning algorithms to facilitate prevention, early detection and monitoring of health issues.
April 2020
Featured across major media outlets (CNN, Forbes and Sydney Morning Herald) for AI/ML tools supporting the response to the COVID-19 pandemic. My work contributed to one of the first COVID-19 health apps in the world, featured by the World Health Organisation (WHO).
University of Melbourne
MACSYS: The Mathematical Analysis of Cellular Systems · School of Mathematics and Statistics
Aalto University, Probabilistic Machine Learning Group
Thesis — Advancing towards personalised medicine: probabilistic machine learning and deep learning for health and genetics · Advisor — Prof. Samuel Kaski
Finnish Center for Artificial Intelligence
Specialising in probabilistic ML and deep learning for health and computational genetics
Velmio
Scaled a healthtech product to 30k+ users · Machine learning for health and wearable devices
Deloitte
University of Sydney
Awarded the K.E. Bullen Memorial Prize in Applied Mathematics
Reserve Bank of Australia · PwC
Econometrics Research at the Reserve Bank of Australia; Actuarial and Data Science Consulting at PwC
University of Western Australia
