Research Showcase
Visual summaries of selected research outputs and data products
Transforming complex data into evidence that drives decisions through |
I'm a data scientist and AI/ML practitioner based in Auckland, New Zealand. With a PhD in Computational Modelling and 7+ years of experience, I build end-to-end data products — from ETL pipelines and predictive models to interactive dashboards and APIs — that turn complex data into business value.
My track record includes scalable ML pipelines achieving 90% forecasting accuracy, multi-module BI platforms, and CI/CD-enabled analytics products. I've published 11 peer-reviewed papers (169+ citations) in journals like Nature Communications, demonstrating rigorous, reproducible work at the highest standard.
I thrive in cross-functional teams — translating between data science, engineering, and non-technical stakeholders to define problems, shape strategy, and deliver measurable outcomes. My domain depth in health and epidemiology is complemented by hands-on product-building experience across business analytics, clinical informatics, and AI evaluation.
I'm always interested in challenging problems at the intersection of data science, AI, and real-world impact — whether that's building intelligent products, advancing research, or driving strategy through analytics.
End-to-end ML pipelines from model development through deployment. Deep learning, NLP, ensemble methods, and predictive/prescriptive analytics.
Building dashboards, APIs, and analytics platforms. Scalable ETL, data quality monitoring, CI/CD, and reproducible workflows.
90% accuracy in trend forecasting using ensemble ML. Statistical modelling, time series analysis, and simulation at scale across 340+ regions.
Translating complex technical outputs into reports, dashboards, and presentations for business, clinical, and policy audiences.
Molecular Epidemiology and Public Health Laboratory
Fisher & Paykel Healthcare
The University of Auckland
Massey University
Inter-University Centre for Astronomy and Astrophysics (IUCAA), India
Data products, ML pipelines, and open-source research code
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.
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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