I am a geospatial data scientist. My main research area is to use geospatial data, Artificial Intelligence (AI), cloud computing and socio-economic modelling to assess, mitigate and manage disaster risk. I particularly studied disaster and agricultural damage, recovery, resilience, and vulnerability assessments using big/geospatial data, advanced machine learning methods, agent-based modelling, and multi-criteria decision-making (MCDM) models. I processed and employed various remote sensing data such as multi-spectral satellite images, UAV/drone, Light Detection and Ranging (LiDAR), Hyperspectral, Synthetic Aperture Radar (SAR), and ground-based data sets in my research.

“Can AI reduce the risk?” This question leads me to research the use of advanced AI-based technological tools and methods including deep learning, explainable AI, digital twins, Internet of things (IoT), and AI-integrated simulation to address disaster and agricultural risk management.