As the first AI Librarian at the University of Chicago, I manage an institutional strategic priority on AI, lead AI literacy initiatives in the library, and am building an AI evaluation framework to assess internal projects and vendor tools. I also co-founded a community of practice for AI practitioners in academic libraries, creating open resources to reduce duplicated effort across institutions.

I have a BA in Linguistics from UC Berkeley and an MSE in Computer Science from Johns Hopkins University. This combination enables me to understand how AI is encountered and interpreted by both technical and non-technical audiences. Experiences across big tech, startups, nonprofits, and academia have given me a strong sense of how novel technologies are integrated and received in different contexts and priorities, and enables me to operate effectively across applied and institutional environments.

Research interests: How archival silence and unrepresentative training data surface as algorithmic bias and model error & developing metrics, methods, and workflows to identify and mitigate these harms. Language models learn from data that disproportionately encode the ideas and perspectives of privileged groups, e.g. those historically afforded an education and platform, while underrepresenting others due to factors like data quality, language, and uneven digitization.