John R. Ladd

I’m an Assistant Professor in Computing and Information Studies at Washington & Jefferson College, where I teach and research on the use of data across a wide variety of domains, especially in cultural and humanities contexts. I also build research tools, write data science tutorials, and make small, weird web projects.

In my research I think about the long, interwoven histories of media and technology from the early modern period to today. I’m currently working on a book about social networks and literary collaboration, called Network Poetics, which argues that shifts in the networks of 17th century print production allowed for the emergence of new literary forms.

I’m an active member of several long-running computational humanities projects and research groups, including Print & Probability, TRACE: Tools and Resources for Analysis of Early English Books Online (see: EarlyPrint), and the Cultural Analytics Research & Teaching Initiative (CARTi).

Some Recent Work

Edge Cases: The Making of Network Navigator and Critical Approaches to DH Tools. An article with Zoe LeBlanc on the role of network analysis tools as/in scholarly infrastructure. The essay details our on Network Navigator, a browser tool for network analysis, with special emphasis on quantitative metrics and less common visualization types.

Working with Local LLMs (On Your Own Computer!). With Melanie Walsh and the AI for Humanists team, a tutorial for humanists who want to use large language models to complete research tasks on their own computers. Local LLMs promote greater privacy and sustainability for humanities AI research.

Imaginative Networks: Tracing Connections Among Early Modern Book Dedications. An article for the Journal of Cultural Analytics that uses bibliographic network analysis to help understand the history of early modern print culture.

EarlyPrint + Python. A textbook for early modern text analysis in the programming language Python, built as an interactive Jupyter Book. Topics covered include TF-IDF, word vectors, and supervised text classification.

Exploring Linked Art. A series of tutorials, made in partnership with the Getty Museum, showing how to work with Linked Art, a linked open data model for cultural heritage objects. The Observable JavaScript tutorials demonstrate how to analyze artworks in Getty’s Online Collections.

Bibliographia. An interactive plot of 50,000+ printed books from the sixteenth and seventeenth centuries, using LDA topic models and LargeVis to cluster similar texts. Made in D3.js and Canvas for the EarlyPrint project.