Abstract

A recurring task at the intersection of humanities and computational research is pairing data collected by a traditional scholar with an appropriate machine learning technique, ideally in a form that creates minimal burden on the scholar while yielding relevant, interpretable insights.

In this talk, I describe initial efforts to design a graph-aware autoencoding model of relational data that can be directly applied to a broad range of humanities research, and easily extended with improved neural (sub)architectures.  I then present results from an ongoing historical study of the post-Atlantic slave trade in Baltimore, illustrating several ways it can benefit traditional scholars. Finally, I briefly introduce a few additional historical and literary-critical studies, currently under-way in the Krieger school, that I hope to consider under the same framework in the coming year.