Fa Li

My research investigates ecosystem greenhouse gas cycling, wildfires, and nature-based solutions by combining data-driven approaches (e.g., physically interpretable AI and causality inference), process-based terrestrial biosphere/Earth system models, and big data (e.g., remote sensing, in-situ measurements).
For example, together with our collaborators at Stanford and beyond, I play a leading role in FLUXNET-CH₄ V2.0, a global network of eddy covariance measurements, to enhance the monitoring of ecosystem carbon (CO2 and CH4)-water-energy fluxes and support Global Carbon Project. I develop physically interpretable AI, integrating scientific principles to improve reliability, particularly when solving critical challenges such as wildfire prediction.