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Department of Mathematics , University of Georgia , Athens , GA 30602 , USA
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Topological spectral representations and latent interaction attention unlock structured, source-target-dependent information exchange for biomolecular learning, achieving SOTA drug discovery performance.
Unlocking superior molecular and materials property prediction by fusing diverse mathematical invariants with topological neural networks, revealing that optimal performance hinges on carefully curated representation-architecture pairings.
Viral genomes are not just sequences, but complex networks of contextual fragments, unlocking superior classification accuracy for novel and evolving viruses.
Euclidean grids blind standard vision models to complex anatomical topology, but routing Hodge-decomposed features through combinatorial cell complexes unlocks higher-order geometric reasoning across 2D and 3D images.