L.S. Shen (Longzhu)

I hold broad interests in building spatiotemporal models in conjunction with varied physical domains, especially in the context of biggeo data analytics. In particular, I'm intrigued by mining biggeo data in order to extract physical insights into the complex earth system. Previously I have built multiple data-driven models to estimate the distribution of chemical nutrients in their various physical forms in the streams on the continental and global scales. Based on these models, I participated in a collaborative investigation on the global N2O emission from fresh water bodies though a hybrid approach that connects the physical and data models. I am also involved in building global fresh water discharge models at 90m spatial resolution.

Currently, I work at CRIB to support the development of biggeo data computational platform. I have keen interests in study bigdata phenomena at both theoretical and practical level. Theoretically, I am interested in investigating the theories to treat scaling and uncertainty issues and couple mechanistic (physical) models with machine learning methodologies. At the practical level, I am interested in developing new geocomputation tools based on spherical geometry. Domain specifically, I am interested in investigating questions at the nexus of evolutionary biology, ecology and public health.