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Understanding the impact of climate change on the diversity of global marine species has important economic value for fishery and policy making. Changes in water temperature, nutrients, and ecological associations can affect an organism’s physical form and growth, which are critical to determining where a species can survive. Using modern 3D imaging technology, this project will quantify large-scale variations in bivalve shell morphology and statistically analyze how they respond to global climate change by developing highly efficient end-to-end geometric deep learning systems that can automatically extract latent semantic representations from millions of micro-CT scans.

Team

Tingran Gao

William H. Kruskal Instructor, Department of Statistics

David Jablonski

William R. Kenan, Jr., Distinguished Service Professor, Department of the Geophysical Sciences