This project will develop approaches to measuring topological similarity that are smaller, faster, and more scalable than previously thought possible, with the goal of creating a method for content-based search of scientific data. Specifically, the investigators will develop a learned-hash function to convert a dataset’s persistence diagram - the common encoding of its topology - to binary code. This approach will allow topological similarity to be maintained when measured as the bitwise distance between codes, converting topological comparisons from an expensive bottleneck operation to one with nominal processing costs that can scale to large database enquiries. Initially, this project will focus on binary codes that maintain clusters and neighborhoods, with the goal of developing a fully domain-oblivious approach to content-based search.

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