Researcher + Builder
I design applied AI systems for complex, high-stakes problems, spanning digital twins, antimicrobial resistance risk assessment, and production machine learning.
Currently preparing for a PhD in Artificial Intelligence at The University of Queensland after leading end-to-end data science work in industry and publishing first-author research on transformer-based forecasting.
Current research direction
Cross-sectoral digital twins for antimicrobial resistance risk assessment, connecting technical rigour with real-world decision-making.
Overview
My background cuts across health, analytics, and AI engineering. That mix has shaped how I work: I like research that can survive contact with real constraints, and systems that do more than demo well.
I completed a Master of Analytics (Health) at Massey University, am concurrently undertaking a Master of Computer Science in AI at Monash University, and will begin a fully funded PhD at The University of Queensland in April 2026.
The throughline is consistent: take messy domains, structure them well, and build models, pipelines, and tools that lead to better decisions.
Focus Areas
01
From forecasting and transfer learning to production workflows, I enjoy building models that are judged by practical usefulness, not novelty alone.
02
I am especially interested in digital twins as a way to integrate multiple sectors, uncertain signals, and high-impact decisions into one workable modelling frame.
03
My industry work has centred on dashboards, internal tooling, pipelines, and decision-support systems that teams adopt because they remove real friction.
Selected Proof
Research
My first-author paper in Energy and Buildings compared transformer architectures across 16 building datasets and demonstrated a 15.9% improvement in MAE for 24-hour forecasts using a multi-source transfer learning approach.
Read PublicationIndustry
At Radix Nutrition, I led data science work spanning optimisation, claims substantiation, and internal product tooling. The result was faster iteration, lower formulation costs, and decision support adopted across R&D and operations.
Build
I use this site, GitHub, and YouTube to document projects, share technical lessons, and make my thinking legible. I care about being able to show the work, not just list it.
Journey
I did not begin in computer science. Coming from health, sport, and human performance gave me a bias toward problems with real-world consequences, messy variables, and multidisciplinary stakeholders.
That perspective still shapes my work today. I am most energised when a project needs both technical depth and translation across disciplines.
Fully funded RTP scholarship and SAAFE CRC top-up focused on cross-sectoral digital twins for AMR risk assessment.
Led end-to-end AI and analytics systems that accelerated formulation, supported health claims, and improved internal decision-making.
Worked on transformer-based forecasting research that led to a first-author Q1 journal publication.
Bachelor of Health, Sport & Human Performance, Master of Analytics (Health), and concurrent Master of Computer Science (AI).
Online Presence
On YouTube and online, I talk about career transitions into AI, the tools and models I use day to day, and the practical side of building technical capability over time.
The throughline is the same as my research and engineering work: curiosity, usefulness, and a bias toward making complex ideas easier to work with.
Connect
I'm always happy to connect with people working in artificial intelligence, health, complex systems, sustainability, and thoughtful technical communication.