Student Projects

This project develops a graph neural network approach to accelerate ecological connectivity analysis by mapping fine-scale landscape features onto lower-dimensional graph representations, overcoming the computational bottlenecks of traditional resistance distance calculations. The research aims to enable high-resolution gene flow predictions across fragmented landscapes while maintaining computational tractability, with potential applications to real-world conservation planning for identifying critical ecological corridors and restoration priorities.

This project aims to develop a high-performance algebraic multigrid (AMG) solver in JAX that supports automatic differentiation and GPU acceleration, addressing critical gaps in existing Python and Julia implementations. The work will enable efficient gradient-based optimization in scientific machine learning applications requiring large-scale linear system solves, with potential to significantly impact the JAX ecosystem for differentiable physics simulations and inverse problems.