Much of the human experience is archived as text. Recent advancements in large-scale data processing and machine learning have given us tools to visually explore the legacy of human literacy in unanticipated ways.

In this studio, we scrutinized text from unorthodox perspectives. We used surface observations and metrics of text to illustrate the semantic and syntactic contours of written expression. We developed tools and strategies for extracting and visualizing quantitative and qualitative data from unstructured text. We examined how machine-guided interpretations of text can inform, undermine, or enforce embedded meanings of language.

The participating students:
Antonio Solano
Divya Srinivasan
Shing Yun
Kate Terrado
Kim McDevitt 
Muling Jiang
Gabrielle Lamarr LeMee
Eric Lee
Anqi Liu