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Research Interests

Our research is characterized by an interdisciplinary perspective on spatial data science driven by advances of quantitative geography (e.g., geographic information science), earth observations (e.g., remote sensing) and computational science. We are interested in deep learning of heterogeneous geographic information to support uncertainty-aware geographic knowledge discovery and decision making. Particularly, we focus on the development of statistical and computational methodologies for (a) integrating heterogeneous sources of geographic information (e.g., incompatible scales) for geographic analysis; (b) characterizing and modeling complex spatiotemporal patterns in geographic phenomena and processes; (c) characterizing and modeling spatiotemporal bias and uncertainty of geographic information and the associated impacts; (d) addressing the computing challenges when the data scale and model complexity increase dramatically; and (e) building geospatial-enabled cyberinfrastructure for domain scientists. Domain applications focus on environmental sciences, public health and social sciences.

Current Projects

Finished Projects