I am currently a Visiting Assistant Professor at Amherst College. In 2018, I received my Ph.D. in mathematics under the supervision of Sergi Elizalde at Dartmouth College. My research is in discrete applied probability and the mathematical foundations of data science.
While a postdoc at Wake Forest University, I developed (joint with Kenneth Berenhaut) a transparent and socially-inspired method for capturing aspects of relative position. The perspective gives rise to a measure of pairwise cohesion (or relationship strength) and a simple threshold for distinguishing strong and weak ties. The connected components of the network of strong ties are the resulting (community) clusters. The method does not require additional inputs, optimization criteria nor distributional assumptions. The associated paper has recently appeared in the Proceedings of the National Academy of the Sciences; here is a link to the paper. A package for the implementation of this approach can be found at: my Github site (moorekatherine/pald).
Language families revealed from cognate information and the PaLD algorithm. The algorithm provides interpretable edge-weights and threshold for distinguishing between strong and weak ties. No additional inputs/parameters (beyond distance information), distributional assumptions, optimization criteria nor iterative procedures are employed.