What I do: As the first AI Librarian at the University of Chicago, I help shape the university's approach to AI literacy and adoption. My work includes teaching workshops and lectures, serving as project manager for a strategic institutional priority on AI, and developing risk evaluation frameworks used to review internal projects and external/vendor products for research and teaching.

Background: In my work, I aim to make AI accessible to people from all backgrounds. My own interdisciplinary background, including a BA in Linguistics from UC Berkeley and an MSE in Computer Science from Johns Hopkins University, along with professional experiences in big tech, startups, nonprofit organizations, and academia, has given me a strong understanding of how novel technologies like AI are built, integrated, and received in a variety of contexts and priorities.

Research interests: Methods to identify and 'patch' gaps in model knowledge caused by archival silence and unrepresentative training data, and how these gaps surface as bias and error in AI systems. Language models learn from large amounts of human‑generated text that disproportionately encode the ideas and perspectives of privileged groups, such as those historically afforded an education and platform, and underrepresents others due to factors like language and data quality.

Transition: People without access to education have been systemically silenced. AI literacy is now becoming a gatekeeping competency. Without it, individuals are at risk of exclusion not only from professional opportunity, but also from participating in shaping the systems that affect everyday life.

Hence, I'm developing an open, inclusive, and modular AI education program for non-CS and interdisciplinary learners with any or no prerequisite knowledge. Unlike most AI education resources, it prioritizes literacy, critique, and responsible use over technical career placement, with the goal of expanding who can meaningfully participate in AI development, evaluation, and governance.

Why it's important: Addressing bias and harm require contributions from different academic fields and the communities affected by these systems. These individuals are often non-technical.

Prequel: Gaita is a conversational RAG system that generates personalized Computer Science learning pathways from open-access courseware. Inspired by my own transition into tech, Gaita was designed to make CS education accessible to learners from non-technical and non-traditional backgrounds.