Education

2016-2020

2015-2016

2011-2015

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

Employment

2020-present

2016-2020 

Assistant Professor, Department of Geosciences, University of Arkansas, Fayetteville, AR, USA

Research/Teaching Assistant, Department of Geography, University of South Carolina, Columbia, SC, USA

Research interests

  • 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

Research Experiences

2018-present

2018-present

2018-present

2016-present

2015-2016

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.