I’ve had a longstanding obsession with silence—specifically, the idea of silence as a signifier and absence as a parsable signal. In Prince and Smolensky’s Optimality Theory, it is the non-words that reveal the underlying structure of a language. In music, Cage shows how silence is compositional. Woolf writes on unwritten books and Du Bois examines how “eloquent omissions and silences” in sorrow songs signify the extent of pain and tragedy more eloquently than words ever could.

My research interests are one instantiation of this obsession: how archival/historical silence and unrepresentative training data surface as algorithmic bias and model error, and developing metrics, methods, and workflows to identify and mitigate downstream harms. LMs learn from data that disproportionately encode the ideas and perspectives of certain groups, e.g. those historically afforded an education and platform, while underrepresenting others due to factors like data quality, language, and uneven digitization. People are being silenced now too, and that is ultimately where I want to take my work.

As the first AI Librarian at the University of Chicago, I'm building infrastructure and programming that shapes how the library and university community evaluates and adopts AI. I manage a strategic priority on AI, lead AI literacy initiatives in the library, and am building an 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. 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.