Meeting roles in:
Near real-time prediction of the coupled carbon and water cycles
ALIVE-KGML: A Knowledge-Guided Machine Learning Framework for Coupled Carbon and Water Flux Estimation from Geostationary Satellites
Enhancing Forest Flux Predictions with Foliar Traits
BenchFlux: Scalable AI Benchmarks for Terrestrial Carbon Fluxes to Advance Research, Education, and Resource Management
I earned my B.Eng. degree in Surveying and Geomatics Engineering, in 2017. I continued my academic journey, obtaining an M.Eng. degree in remote sensing from the College of Engineering at the University of Tehran, Iran, in 2021. I am particularly passionate about using artificial intelligence (AI) and machine learning (ML) methods to analyze different remote sensing datasets, especially for urban and agro-environmental purposes. Currently pursuing a Ph.D. program at the University of Wisconsin – Madison, my research focuses on near real-time carbon cycle mapping using Geostationary satellites observations.