The development of informative vector representations for text, languages, protein interactions, and other complex data has revolutionized how we analyze, organize, and
extract insights, exemplified by advancements in large language models and generative AI. Similarly, creating effective vectorizations for geometry holds the promise of equally transformative and far-reaching impacts. In my talk I will describe a geometrical vectorization framework based on homology called stable rank. I will provide several illustrative examples of how to use stable ranks to find meaningful results in biological data.
