This project uses the outputs of runs of the Weather Research and Forecasting (WRF) Model to examine the connection between marine heatwaves in the Gulf and coastal temperatures and humidity in the Houston/Galveston Bay region.

URban mitigation and adaptation strategies Gauging through Empirical functions and Data products

SPARC is a physics-constrained spatial machine-learning pipeline that trains an ensemble of geographically-weighted models, validates causal relationships via directed acyclic graphs (DAGs), and simulates "what-if" intervention scenarios with built-in uncertainty quantification. It is designed to be domain-agnostic.

Kinshasa Earth Observation data/analysis

🌳 Data pipeline analyzing shade impact on NYC neighborhood temperatures

Rethinking urban heat risk in Indian cities using temperature anomalies, census data, Global Forest Watch, and Yale climate opinion data

Real-time urban heat risk scoring across 24 zones in 6 Indian cities — XGBoost, SHAP, Streamlit