With a multi-disciplinary background in earth science, electrical engineering and satellite technology, Bin has spent more than ten years in scientific research and teaching in university and research institute. His previous study aimed to integrate and interpret remote sensing signals across various satellite platforms and ground measurements for developing algorithms to enhance spatial resolution of satellite imagery and compute geophysical variables for the purposes of earth surface process explanation, e.g., Land cover/use, crop field estimation, hydrological processes and weather forecasting. As restricted by current satellite technology and limited spatial and temporal sampling intervals from multiple data sources which bring noises and uncertainties, this motivated Bin to propose new approaches applying to his research which involve machine learning technology instead of traditional physical modeling. Bin Fang received his PhD degree in geological sciences from University of South Carolina and was a postdoctoral research scientist at Columbia University.
