Associate Professor with Tenure
Department of Applied Artificial Intelligence
Sungkyunkwan University
Founder and Director
Data Science and Artificial Intelligence Laboratory (DSAIL)
Human-Centered AI | Human–AI Interaction | Multimodal Learning | Responsible AI | Mental Health and Healthcare AI | Social Computing
E-mail: jinyounghan AT skku DOT edu
Bio
Jinyoung Han is an Associate Professor with tenure in the Department of Applied Artificial Intelligence at Sungkyunkwan University (SKKU), where he founded and directs the Data Science and Artificial Intelligence Lab (DSAIL). His research develops human–centered AI systems that understand complex human signals, support people and domain experts in consequential settings, and remain interpretable, reliable, and aligned with human values. His work integrates human-AI interaction, multimodal learning, natural language processing, computer vision, and social computing, with applications in mental health, healthcare, and online information ecosystems. Rather than treating application domains merely as testbeds, he works closely with clinicians, therapists, counselors, social scientists, and other domain experts–from problem formulation and data design to model development, interactive system building, and human-centered evaluation. He has led major research projects in socially responsible AI, human–AI alignment, mental–health AI, clinical decision support, misinformation, and deepfake detection.
Research Vision
Human-Centered AI for Understanding, Supporting, and Protecting People
My research seeks to advance artificial intelligence that:
understands complex human states, behaviors, and relationships;
supports people and domain experts in high-stakes decision-making;
protects individuals and society from AI-mediated risks and harms; and
is evaluated not only by predictive performance but also by its interpretability, reliability, usability, and long-term human impact.
Selected Publications
Human–AI Interaction and Expert Support: I design interactive AI systems that augment human expertise through transparent evidence, adaptive assistance, and meaningful human control.
“BetaDAPR: An AI-based Expert Support System for Art Therapists with Qualitative and Quantitative Assistance,” ACM CSCW 2026.
“Venus and Mars on Canvas: AI-Mediated Collaborative Drawing for Romantic Relationship Insight,” ACM CHI 2026.
“Autiverse: Eliciting Autistic Adolescents’ Daily Narratives through AI-guided Multimodal Journaling,” ACM CHI 2026.
"PracticeDAPR: An AI-based Education-Supported System for Art Therapy," ACM CSCW 2025.
"Counselor-AI Collaborative Transcription and Editing System for Child Counseling Analysis," ACM IUI 2025.
"SceneDAPR: A Scene-Level Free-Hand Drawing Dataset for Web-based Psychological Drawing Assessment," The Web Conference 2024.
"AlphaDAPR: An AI-based Explainable Expert Support System for Art Therapy," ACM IUI 2023.
Mental Health and Multimodal Human-State Modeling: I develop multimodal and longitudinal AI models for understanding mental health, communication, behavior, and interpersonal relationships.
“SynSym: A Synthetic Data Generation Framework for Psychiatric Symptom Identification,” ACM KDD 2026.
"CURE: Context- and Uncertainty-Aware Mental Disorder Detection," EMNLP 2024.
"Detecting Bipolar Disorder from Misdiagnosed Major Depressive Disorder with Mood-Aware Multi-Task Learning," NAACL 2024.
“HiQuE: Hierarchical Question Embedding Network for Multimodal Depression Detection,” ACM CIKM 2024.
"Learning Co-Speech Gesture for Multimodal Aphasia Type Detection," EMNLP 2023.
“Towards Suicide Prevention from Bipolar Disorder with Temporal Symptom-Aware Multitask Learning,” ACM KDD 2023.
"D-Vlog: Multimodal Vlog Dataset for Depression Detection," AAAI 2022.
"Cross-Lingual Suicidal-Oriented Word Embedding toward Suicide Prevention," EMNLP 2020.
Responsible AI and Information Ecosystems: I study how misinformation, deepfakes, and algorithmic risks emerge and spread, and develop methods to detect, explain, forecast, and mitigate them.
“Multimodal Spatiotemporal Forecasting of Deepfake Propagation on Social Media,” The Web Conference 2026.
“HiDF: A Human-Indistinguishable Deepfake Dataset,” ACM KDD 2025.
Clinical and Healthcare AI: I develop interpretable and clinically meaningful AI systems in collaboration with healthcare professionals.
“LASOR: Towards Clinically Transparent and Explainable Ophthalmic Report Generation via Lesion-Aware Segmentation,” IEEE/CVF WACV, 2026.
"Expertise Matters in AI Adoption: A Comparative Study of Retina Specialists and General Ophthalmologists in AI-CAD Adoption," International Journal of Human–Computer Interaction 2026.
"Building the world's first truly global medical foundation model," (Global RETFound Consortium), Nature Medicine 2025.
"CAMEL: Confidence-Aware Multi-task Ensemble Learning with Spatial Information for Retina OCT Image Classification and Segmentation," IEEE/CVF WACV, 2025.
“Developing and Evaluating an Artificial Intelligence-Based Computer-Aided Diagnosis System for Retinal Disease: A Diagnostic Study for Central Serous Chorioretinopathy,” Journal of Medical Internet Research, 2023.
Full publication list: Google Scholar
Selected Honors and Leadership
Prime Minister’s Commendation, 2026 Science and ICT Day Government Awards, Republic of Korea
Outstanding Reviewer, ACM KDD Applied Data Science Track, 2025
SKKU Best Faculty Awards for Research, 2020 and 2024, and for Teaching, 2023 and 2024
Head, Department of Applied Artificial Intelligence, Sungkyunkwan University, 2020–2021 and 2023–2024
Chair, Division of ICT, and Head, Department of HCI, Hanyang University ERICA, 2019
Co-Chair, Responsible Multimodal Foundation Models for Knowledge Discovery Workshop, ACM KDD 2026
Academic Background
Before joining Sungkyunkwan University, Jinyoung Han was an Assistant Professor in the Division of ICT and Department of Human–Computer Interaction at Hanyang University ERICA. He was a postdoctoral researcher at the University of California, Davis and Seoul National University, and a visiting scholar at the University of South Florida. He received his Ph.D. in Computer Science and Engineering from Seoul National University and his B.S. in Computer Science from KAIST.
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