As the first AI Librarian at the University of Chicago, I help shape the university's approach to AI literacy and adoption. I teach AI at an introductory level, develop risk evaluation frameworks for ethical and responsible AI adoption, and support faculty and students in using AI for research and learning.

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. I use these insights to create educational resources and opportunities that help people from both technical and non-technical fields apply their perspectives in critiquing and building the future of AI ethics, policy, and practice.

I’m interested in measuring and mitigating gaps in model knowledge (shaped by archival/systemic silences and unrepresentative training data) and the resulting biases in AI systems that perpetuate harm. I’m also building an open, inclusive, and personalized AI education platform to mitigate representation gaps and their downstream effects on AI systems. This work builds on Gaita, a conversational RAG system I created with some wonderful collaborators that generates personalized Computer Science learning pathways from open-access courseware. Inspired by my own transition into tech, I started this project to make CS education accessible to learners from nontraditional and underserved backgrounds. Reach out if you think we'd have good conversations :)