Computational Analysis of Poetry
Unbiased AI for Poetry Analysis: Toward Equitable and Diverse Digital Libraries
In this Early Career Research Development project funded by IMLS, I develop an open-source AI framework for digital libraries to explore, curate, and analyze large collections of digitized poetry texts, which includes a development of an unbiased AI model that understands poetry
What Is This Song Really About?
Computational Lyricology
Song lyrics and poetry contain multiple layers of meaning, often misleading machines that attempt to understand it. We developed a community-driven data enrichment framework, which actively harmonizes human readers’ understanding of those hard-to-understand text documents into the supervised machine learning framework.
I am recruiting PhD students for Fall 2027 interested in Music Information Retrieval (MIR) and audio processing within Information Science, particularly computational approaches to understanding and generating vocal expression across speech and music. My research includes music and audio data collection and annotation, computational and empirical analysis, digital library development, and MIR system evaluation.
I’m currently expanding our ICASSP 2024 paper comparing the musicality of poetry reading, narration, and singing, while also working on a BRIDGE-funded project on multimodal interfaces for vocal training and performance. My MIR research has been published in ISMIR, DLfM, ICASSP, ASIS&T, and JCDL.
If you are interested in working with me, please reach out and apply to our PhD program.
About Me
Kahyun Choi is an Assistant Professor in the School of Information Sciences at the University of Illinois at Urbana-Champaign.
- Past Positions: Former Assistant Professor, Indiana University Bloomington; Former Software Engineer, Naver (Korea)
- Education: Ph.D., School of Information Sciences, University of Illinois Urbana–Champaign
- Research Topics: Computational analysis of poetry, music, and musicality in speech, Human-AI co-creation of cultural datasets and metadata, Ethical AI workflows for AI-enhanced digital libraries, AI literacy education in public library contexts
- Core Fields: Music Information Retrieval (MIR) and Digital Humanities (DH) within Information Science (IS)
- Collaborative Fields: Library Science, Education, and HCI
- Awards & Grants: IMLS Early Career Research Development Grant (2022), IMLS National Leadership Grant (2021, 2026), Luddy Faculty Fellowship (2021), 2026–2027 UIUC BRIDGE Award
- Teaching: Database Design, Music Data Mining; Recipient of the 2023 IU Trustees Teaching Award and recognized as “Outstanding” on the University of Illinois List of Teachers Ranked as Excellent in 2025.
- Service
- UIUC iSchool Committees: MSIM, MSLIS, Research Advisory, Faculty Search, Doctoral Studies
- Professional Organizations: IMLS review panels; Korean Society for Music Informatics (Board Member); Audiovisual Metadata Platform (Advisory Board)
- Academic Community: Organizer, meta-reviewer, or reviewer for venues including ISMIR, DLfM, and TISMIR; Former Evaluation Committee leader for MIREX.
- Outreach: Development of an AI literacy curriculum through public libraries; led AI literacy workshops for youth in public libraries, including Maryland and San Diego, and led a YALSA webinar, Hands-On AI Literacy Programs for Youth, for youth librarians in 2023.
Contact
501 E. Daniel St. MC-493
Champaign, IL61820-6211
kahyun AT illinois DOT edu