Associate Professor
Department of Industrial Design
Affiliated Faculty
KAIST AI Institute
N25 Room 305
hwajung at kaist dot ac dot kr
Want to meet?
Office hours: Upon request
Teaching
ID508 (UCD Methods)
ID221 (Information Design)
Ph.D. Students
Kwangyoung Lee
Hyunseung Lim
Jiazhou Wu
Master Students
Sooyeon Nam
Jong-Ok Hong
Yeohyun Jung
Joonyoung Park
Gahyeon Bae
Current Interns
Inhwa Song
Seo-Kyoung Park
Enrique Jose
Sieun Kim
Sungmin Na
Alumni
Taewan Kim (UX researcher at Samsung)
ChoWon Kang (Research Scientist at Korea Research)
Suyeon Ahn (PM at https://ailike.me/)
News and Events
Promoted as a Tenured Associate Professor
Three papers accepted to CHI 2025
Four papers accepted to CHI 2024
Attended CHI 2023 and presented two fabulous work on autistic individual’s routine and romance and two workshop papers on AI-assisted journal writing and stress intervention for customer service (CS) workers
KAIST 등, 자폐스펙트럼장애 직장 적응 돕는 VR 개발 (2023.1.2 전자신문)
I study, design, and build systems for better human-data interaction in diverse settings such as healthcare, journalism, and culture. I am broadly interested in the social implications of data and artificial intelligence. Before joining KAIST in 2021, I worked as an assistant professor at Seoul National University (2018-2021) and UNIST (2015-2018). I received my Ph.D. in Human-Centered Computing from Georgia Tech in 2015 and B.S. in Industrial Design from KAIST in 2009.
I manage DxD (data, interaction, design) lab where we explore design from, with, and by data to develop technologies that are more sensitive to human concerns and behaviors. We work to provide novel conceptual and computational tools and methodologies for designing human-centered AI experiences, especially for individuals with special needs.
My research group explores Generative AI and UX design of large language models (LLMs) for individuals with special health needs, as well as AI risk mitigation to ensure ethical and transparent AI interactions. Our work is rooted in participatory design and employs mixed design research methods, integrating both qualitative and quantitative approaches to develop inclusive and effective AI-driven systems. By leveraging user insights and empirical data, we aim to bridge the gap between cutting-edge AI technologies and real-world needs.
Human-Centered AI, Human-Data Interaction, Data Humanism, Digital Healthcare, Personal Informatics, Autism, Mental Health