Introducing Guofeng Cao
My research is characterized by an interdisciplinary perspective on geographic information science driven by the advances of spatial Big Data (e.g., social media and remote sensing), machine learning/artificial intelligence, and computational sciences. The overarching goal isÌýdeep learning of heterogeneous geographic information to support uncertainty-aware geographic knowledge discovery and decision making. Particularly, I focus on the development of statistical/machine learning and computational methodologies to integrate heterogeneous sources of geographic information for complex spatiotemporal patterns. I am particularly interested in characterizing and modeling geospatial biases and uncertainty of geographic information and the associated impacts in scientific applications and practical decision making. I also developÌýmethods and tools to address the computing challengesÌýthat arise when the data scales and computation complexity are not manageable with regular computers. I work closely with domain scientists to build geospatial cyberinfrastructure to tackle domain challenges, with a particular focus on natural hazards, environmental sciences, public health and global changes. My research has been supported by several funding agencies, including NSF, USGS, NIST, USAID, USDA and NIH.
