Geometric and topological methods are reshaping the study of complex, high-dimensional data, yet their theoretical foundations still lag behind their accelerating adoption in practice. This workshop will bring together researchers in computational geometry, applied topology, and machine learning to fortify and extend these foundations while developing scalable, interpretable approaches for data-driven science. By bridging theory and computation, the program aims to clarify core principles, overcome algorithmic bottlenecks, and establish rigorous frameworks that can guide future applications. The workshop will feature talks from senior leaders and rising scholars, collaborative discussions, and opportunities for junior participants to engage with the community and help shape the evolving landscape of the field.