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.

Given a data set (e.g., of objects, observations, or individuals), we often want to understand both local and global structure including clusters, near neighbors and the ‘shape’ of communities. However, in many complex settings, we do not have access to a reliable (numeric) measure of dissimilarity which would then allow us to use one of many standard approaches. In such cases, we may nevertheless be able to obtain responses to queries of the form: Among *x, y* and *z, *which two are most alike?

My recent work includes the introduction of a transparent socially-inspired method for revealing community structure in the form of a weighted network which distinguishes strong and weak ties. 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).