Research Showcase
Visual summaries of selected research outputs and data products
Transforming complex data into evidence that drives decisions through |
all-MiniLM-L6-v2)I study how infectious diseases spread — from pandemic connectivity patterns to zoonotic spillover of viruses like Ebola, Lassa, and SARS-CoV-2. Ask anything and get answers grounded in my peer-reviewed papers.
I am a computational epidemiologist turned engineer, based in Auckland. My journey began in high-performance computing — simulating galaxy clusters on supercomputers — and has evolved through infectious disease modelling and peer-reviewed research into the cloud engineering and applied ML work I do today. The throughline is the same: rigorous thinking about complex systems, whether the system is a galaxy, an epidemic, or a production ML pipeline.
I am most engaged when the problem is genuinely hard and the stakes are real — health tech, clinical informatics, any domain where the distance between data and a critical decision matters most. Whether I am building infrastructure, deploying scalable solutions, or shaping strategy, I try to bring the same standard: reproducible, honest, and built to last.
When I am not architecting systems or training models, you can usually find me exploring Auckland's craft beer scene, chasing the perfect cup of specialty coffee, or deep-diving into whatever complex problem has caught my attention that week.
Loffty
Institute of Data
Molecular Epidemiology and Public Health Laboratory
The University of Auckland
Massey University
Inter-University Centre for Astronomy and Astrophysics (IUCAA), India
Georgia Institute of Technology, Atlanta, USA
Data products, ML pipelines, and open-source research code
Retrieval-augmented generation pipeline over my peer-reviewed papers — 779 chunks from 6 publications, embedded with all-MiniLM-L6-v2, stored in ChromaDB, and answered by Llama 3.3 70B via Groq. Deployed as a FastAPI backend powering the live chat widget on this site.
Cross-source investigation into whether the Toyota Aqua's "most stolen car" status reflects genuine targeting or statistical illusion. Four data sources, 21,000+ theft records, time-series forecasting, and socioeconomic regression across 13 NZ regions.
End-to-end ML evaluation product simulating 10,000-patient cohorts to benchmark AI screening tools. Modular pipeline architecture (config/, src/, notebooks/) with stratified accuracy metrics (sensitivity, specificity, AUC-ROC).
Multi-page data product with 7 interactive modules: KPI tracking, SPC charts, anomaly alerting, automated stakeholder reporting, and statistical hypothesis testing (Mann-Whitney U, Kruskal-Wallis, Chi-Square).
Production-grade data product: 34,000+ measurements processed through a four-gate ETL pipeline with audit logging, Power BI star-schema export, interactive Streamlit dashboard, and CI/CD via GitHub Actions with pytest validation.
Large-scale predictive model across 340 regions with multi-source data integration and parameter optimisation. Open-source, reproducible pipeline accompanying first-author publication.
Simulation-based predictive modelling with parameter estimation and sensitivity analysis. Open-source research code accompanying peer-reviewed publication.
Market basket analysis and temporal pattern detection with external data integration (weather API). Business-oriented data product demonstrating customer behaviour insights and revenue optimisation.
11 peer-reviewed articles • 169+ citations • Google Scholar • ORCID
Journal of the Royal Society Interface, 21(210)
Journal of the Royal Society Interface, 21(216)
Journal of the Royal Society Interface, 19(187)
Visual summaries of selected research outputs and data products
Let's build something meaningful together
I build end-to-end data products — from ETL pipelines and predictive models to dashboards and APIs. 7+ years experience, PhD-qualified, with a track record of delivering measurable outcomes across cross-functional teams.
Send Me an EmailMy expertise spans computational modelling, AI evaluation, predictive analytics, and health informatics. I'm eager to collaborate on impactful, data-driven research across domains.
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