Geographer working at the intersection of urban land change, road network modeling, and biodiversity conservation — drawing on machine learning, statistical modeling, and large-scale spatial analysis to study the global footprint of cities.
Where, how fast, and at what cost the world's cities are growing. Long time-series across Bangladesh, Indonesia, and 19 world regions under SSP scenarios.
The first global-scale road network growth model, featured by AGU's Eos. Forecasting where new roads will appear and what landscapes they will cross.
How urban expansion and roads isolate protected-area networks and threatened species ranges. Brazil, Indonesia, Nigeria, Bangladesh.
How human activities — settlements, roads, infrastructure — interact with biophysical systems. Where pressure accumulates, what couplings emerge, and what those imply for sustainability.
Spatial methods applied to public health. Earlier work mapped COVID-19 spread, examined geospatial methods in research on homeless populations, and reviewed the role of GIS in pandemic decision-making.
Machine learning, statistical, and process-based models for spatial problems. From hedonic regression to XGBoost land-cover classification, AI-based flood susceptibility, and urban–transport coevolution.