Hackerman Hall B17 @ 3400 N. Charles Street, Baltimore, MD 21218
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 thatcreates 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 appliedto 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 sameframework in the coming year.