Our group broadly uses data-driven and model-driven approaches to quantify the patterns of element flux and isotope behavior involved in the global carbon and biogeochemical cycles, especially under periods of climatic perturbations. Extensive data mining, data assimilation, large-scale spatial-temporal statistical analysis, and machine learning are frequently used in our research projects. We hope geo-statistics and machine learning could reveal the intrinsic patterns of nature’s processes that are sometimes extremely difficult to be captured by classical physical process models. With that being said, in areas where data is extremely limited or data-driven approaches are not suitable, numerical modeling (e.g., modeling the global carbon cycle) also serves as a critical tool in our research.
Recent News
2026-07-06 Graduate student Xiying Sun was awarded the Graduate Student Research Grant from the Geological Society of America (GSA) for 2026-2027. The total award is $1,500. Her research topic is "From end-member regimes to mixed controls in silicate weathering".
2026-06-19 Dr. Shihan Li has published a new paper in Nature Communications. Li, S., Shen, J., Grossman, E. L., & Zhang, S. (2026). Erosion-driven delayed warming and marine stress prior to the end-Permian mass extinction. Nature Communications, 17(1), 5456. https://doi.org/10.1038/s41467-026-74636-2
2026-05-27 Congratulations to Shihan Li on successfully defending his dissertation, "Towards the Unknown Processes in Modeling the Hyperthermal Events", and completing the requirements for his Ph.D. We congratulate Dr. Li on this important achievement and wish him continued success in his future research and career endeavors!