Ph.D., Department of Geography, University of South Carolina, Columbia, SC, USA
M.S., School of City Planning & Architecture, Georgia Institute of Technology, Atlanta, GA, USA
B.S., School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, Hubei Province, China
Assistant Professor, Department of Geosciences, University of Arkansas, Fayetteville, AR, USA
Research/Teaching Assistant, Department of Geography, University of South Carolina, Columbia, SC, USA
Remote sensing and GIS in natural hazards
Data-driving visualization and advanced data fusion flood models
Big social data mining
Regional geospatial analysis
Nighttime remote sensing
Dasymetric mapping in population estimation
Regional Geospatial analysis
Created high-resolution population grid dataset that covers the entire CONUS using newly released Microsoft national building footprints, designed interactive user interface for data sharing and downloading, and coupled high-resolution population product with floodplains from different agencies to generate detailed profiles of 100-year flood exposure in the CONUS.
Dynamics of Human Settlement Intensity in Hurricane-prone Areas from Nighttime Remote Sensing
Identified hurricane proneness using historical storm tracks, desaturated traditional DMSP/OLS nighttime remote sensing imagery using NDVI derived from AVHRR and MODIS, and explored the spatiotemporal dynamics of human settlement in areas with high hurricane pronesness in the CONUS.
Sensing and Improving Disaster resilience through Social Media Data Mining (deep learning analytics)
Designed a visual/textual fused artificial intelligence to automatically classify disaster related social media leveraging state-of-the-art convolutional neural networks.
Remote Sensing and Social Sensing in Rapid Flood Awareness and Inundation Mapping
Designed flood geostatistical models integrating remotely sensed moisture with socially sensed VGI to aid in better flood awareness and inundation mapping.
Energy Harvesting Scenario (solar and piezoelectric) using GIS based techniques
Case study: Georgia Tech campus
Determined the optimal locations to installed solar and piezoelectric materials using GIS techniques and estimated the potential energy generated and potential costs.