At OpenGeoHub, Qin contributes to help the development and application of cutting-edge machine learning and artificial intelligence methods for environmental modeling. She supports the research group’s objectives by processing global and pan-continental datasets to produce state-of-the-art maps of land use, land cover, and other biophysical variables, with quantified uncertainty.
Qin’s key responsibilities include:
- Processing and analyzing continental-scale datasets using high-performance computing infrastructure.
- Developing reproducible and optimized geocomputation workflows using computational notebooks and open-source tools.
- Collaborating intensively with internal teams and international research partners on interdisciplinary projects.
- Sharing methods and results through scientific publications and open data platforms.
Qin holds a Master’s in Geo-infomation Science & Remote Sensing and has a background in deep learning, remote sensing, and high-performance computing. Her role enables impactful research within a dynamic, international team, with access to extensive HPC resources and large-scale geospatial data archives.