Kahyun Choi
Kahyun Choi is an Assistant Professor in the School of Information Sciences at the University of Illinois at Urbana-Champaign. Before joining UIUC, she was an Assistant Professor at Indiana University Bloomington. She earned her Ph.D. from the School of Information Sciences at the University of Illinois at Urbana-Champaign. Before pursuing her Ph.D., she worked as a software engineer at Naver, an Internet portal in Korea.
Her research applies computational methods and machine learning to cultural data across multiple modalities, including audio and text. Her work focuses on the 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 and cultural collections; and AI literacy education in public library contexts. Her interdisciplinary work is anchored in venues across two core research areas, Music Information Retrieval (MIR; ISMIR, ICASSP, DLfM, ICMC) and Digital Humanities (DH; DH, JOHD), within the broader field of Information Science (IS; ASIS&T, iConference, JASIST, JCDL). Additionally, her collaborative work through grant projects with multidisciplinary researchers has been published in venues in Library Science (Library Quarterly), Education (DISER, NARST), and Human-Computer Interaction (CHI).
Her work has been supported by competitive awards including and a fellowship, including the 2022 Institute of Museum and Library Services (IMLS) Early Career Research Development Project Grant, the 2021 IMLS National Leadership Grant, and the 2021 Luddy Faculty Fellowship. As an educator, she received the 2023 IU Trustees Teaching Award and was recognized as “Outstanding” on the University of Illinois List of Teachers Ranked as Excellent. She has taught courses including Music Data Mining, an introductory deep learning and machine learning course for music, audio, and text data analysis. Beyond the university, she developed a library-based AI literacy curriculum and led AI literacy workshops for youth at public libraries across the country, including in Maryland and San Diego.
At the iSchool, she has served on committees for the MSIM, MSLIS, Research Advisory, and Faculty Search. Her external leadership includes serving as a board member of the Korean Society for Music Informatics, serving on the advisory board of the Audiovisual Metadata Platform, and participating in IMLS review panels. Additionally, she actively supports her research communities as an organizer, meta-reviewer, or reviewer for journals and conferences, including ISMIR, DLfM, and TISMIR.
My Research Team
You Peng
PhD student in Information Sciences at UIUC (since 2025)
You Peng received his B.S. in Computer Science from Virginia Tech and his M.S. in Information Management from UIUC iSchool. He joined my group in 2025 and currently serves as a Research Assistant on the IMLS CAREER project.
We are working on designing and building AI-enhanced digital libraries to improve the accessibility of poetry collections, developing annotation tools and viewers to support human-centered annotation practices with AI-assisted workflows, and analyzing the mood and themes of poetry across multicultural groups. We are also examining digitized textual corpora from new perspectives in light of recent advances in AI technologies.
William Sideri
PhD student in Information Sciences at UIUC (since 2025)
Will earned his B.A. in English Language and Literature from Colby College and his M.A. in Digital Communication and Media/Multimedia from the University of Chicago. He joined my group in 2025 and currently serves as a Research Assistant on the IMLS CAREER project.
We are currently examining Shakespeare’s works from a classification perspective, including questions around the definition and ambiguity of author-annotated labels. We are also reviewing literature to come up with a concrete project exploring the relationship between stylistic features in poetry and other aspects such as poetic prosody. In addition, we are developing an evaluation framework to assess how well AI understands poetry.