I am a Teacher-Scholar Postdoctoral Fellow at Wake Forest University in North Carolina and work with Kenneth Berenhaut.  I enjoy thinking about questions such as:

How can we understand communities in data?  The meaning of “local”?   What do we mean when we talk about “clusters”?
Can simple (socially-inspired) local rules for a random walk on a graph lead to interesting global properties?
Can we articulate measures of the degree of “unpredicability” of a process given that we can only witness a single outcome?
What can we learn from ordinal dissimilarity relations?  That is, among triples of objects, perhaps we only know the two which most similar and the two which are least similar – but not the scale of these dissimilarities.

Broadly speaking, the questions I’m interested in these days are in discrete applied probability and are motivated by a social perspective.  My thesis work at Dartmouth (advised by Sergi Elizalde) was based on combinatorial problems arising from permutation-based approaches to time series analysis.

The picture below helps us visualize the strong and weak relationships among rather high-dimensional points, revealing distinct clusters in this setting.  The perspective we take is socially-based – and is also entirely free of parameters, iterative proceedures and distributional assumptions.  If you’d like to hear more, send me an email and we can chat (and perhaps apply it to any data you have, lots more coming soon).

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