Data Science for Networks. Modeling, analysis, and design of networked systems. Signal processing, machine learning, optimization, and algebraic topology applied to the understanding of networks and network data. Topics of interest include clustering in social and technological networks, authorship attribution problems, abstract representations of network data structures, networks in Neuroscience, linear and nonlinear network dynamics, social networks and team dynamics, and processing of signals defined on graphs.
Publications/Creative Works
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Affiliations
Training Grants
NLM Training Program in Biomedical Informatics & Data Science for Predoctoral and Postdoctoral Fellows
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