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I am a computational topologist/geometer interested in the use of theoretical topology and geometry to extend existing statistical frameworks (GLMs, survival analysis, factor analysis/structural equation models, hypothesis tests, Bayesian adaptive trial designs), machine learning methods, graph/network analytics, and partial differential equation models of biological/industrial systems. These more general models allow for more flexible modeling and can accommodate diverse data structures. If there's an obscure mathematical theory that can solve a pressing problem, I'll find it and figure out how to leverage it in an analytics problem.