Fusion And Inference From Multiple And Massive Disparate Distributed Dynamic Data Sets
Technical Report,01 Sep 2012,31 Mar 2017
Johns Hopkins University Baltimore United States
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We have developed the first principled methodology for two-sample graph testing designed a provably almost-surely perfect vertex clustering algorithm for block model graphs proved analogues of classical limit theorems for the adjacency and Laplacian embeddings for random graphs, which have led, in turn, to significantly improved algorithms for latent position estimation established the accuracy of and efficiently implemented a fast, successfully scalable program for an approximate solution to the NP-hard problem of matching graphs developed efficient methods for vertex nomination in graphs determined precisely how to mitigate information loss across shuffled networks. This has led to dozens of papers published in top journals. Moreover, we have employed these theoretically-justified techniques on a suite of applications, conducting end-to-end analyses of real data from domains as varied as neuroscience, speech and language processing, threat detection, and social networks.
- Numerical Mathematics