Analytical & Modelling
- Statistical modelling: GLM, GAM, mixed-effects models, hierarchical modelling
- Spatial modelling: species distribution models, spatiotemporal analysis, spatial autocorrelation, spatial forecasting
- Machine learning: ensemble methods, neural networks, gradient boosting, MaxEnt
- Bayesian inference: hierarchical models, spatiotemporal models, detection–abundance separation
- Forecasting & simulation: population viability, spatial forecasting, scenario testing
- Probability modelling: hypergeometric distribution, Monte Carlo simulation
Technical Stack
- Languages: R, JAGS, Python, SQL
- Databases & storage: SQL, Microsoft Access
- Version control: Git & GitHub (collaborative workflows, branching, pull requests)
- Data workflows: large-scale data wrangling, multi-source data integration, spatial analysis, parallel processing, reproducible pipelines
- Visualisation: R (ggplot2, base), Python (matplotlib, Plotly)
- Remote sensing & geospatial: raster/vector processing, spatial joins, satellite-derived metrics
- Other analytics tools: Excel (advanced formulas, pivot tables, data cleaning, charts)
- Web development: GitHub Pages (designed and developed this website) and Shiny Apps
Applied Expertise
- Pattern detection in complex, high-dimensional datasets
- Forecasting to predict trends, risks, and opportunities across domains
- Tailored statistical analysis customised to specific datasets and objectives
- Decision-support tools: interactive tools and dashboards for stakeholders
- Workflow optimisation for computational efficiency and scalability
- Translation to action: turning analyses into clear, actionable recommendations
- Data-driven frameworks for strategic decision-making
Communication
- Writing: lead and co-author on peer-reviewed papers; clear technical documentation
- Speaking: conference talks, workshops, stakeholder briefings; tailoring content to technical and non-technical audiences
