Human-Computer Interaction Research
The human-computer interaction research group at NAVER AI Lab is a vibrant research group demonstrating how contemporary AI technologies can be beautifully embedded in computing systems, and understanding how we should design AI technologies to benefit end-users. Our research interests included but not limited to:
AI-infused interactive systems
Digital health and well-being applications
Accessibility and safety of AI
Large Language Model-driven computing systems and empathetic agents
Call For Open-Rank Research Scientists
We invite applications for self-motivated research scientists in the field of HCI.
Location: In-person, NAVER main office at Seongnam, Gyeonggi, South Korea
We expect you to do the following:
Execute academic research agendas at the intersection of HCI and AI.
Actively collaborate with other researchers at NAVER AI LAB to demonstrate the capabilities of AI technologies in designing novel HCI systems.
Lead a wide range of research activities including but not limited to interactive prototyping, user studies, surveys, design sprint, literature review, and deployment study.
Disseminate research outcomes at top-tier academic venues such as conferences and journals.
Working Environment:
You can pursue your research visions in a bottom-up research environment where you can propose a research agenda and organize the team on your own.
You can collaborate with other researchers at other teams at NAVER or other academic institutes.
We provide various forms of collaboration including research internship, research centers (e.g., Seoul National University, KAIST, and University of Toronto).
You will have opportunities to collaborate with product teams at NAVER, which develop numerous kinds of in-the-wild services on various platforms such as web, mobile, desktop, and smart speakers.
Minimum Qualifications
Holds a PhD degree (or expected to receive within 3 months) in HCI-related disciplines such as Computer Science, Information Science, and Industrial Design
4 primary-authored (1st or corresponding) main track full papers at [CHI, UIST, CSCW, or IMWUT] within the last 6 years, at least 2 of them at CHI (Note that our research interns hold about 2 or more CHI papers on average when applying)
Expertise in the quantitative and qualitative HCI research methods
Proficient verbal and written communication in English
Preferred Qualifications
Being knowledgeable in Machine Learning, Computer Vision, or NLP technologies to streamline the collaboration with AI researchers
Having rich experience in designing and developing AI-infused interactive systems
How to Apply to our Recruitment Pool
Please submit your application via the recruitment platform to register for our Talent Pool (available in Korean/ English), where sign-in is required.
Application category: Tech > Common > Common > AI (Full-time)
Please be advised that the hiring process could extend up to three months. If you have a deadline for another offer, we encourage you to reach out to HR for further assistance.
Selected Publications (2023-)
NAVER AI Lab employees (full-time and interns) are distinguished by being displayed in bold text.
2024
ChaCha: Leveraging Large Language Models to Prompt Children to Share Their Emotions about Personal Events Woosuk Seo, Chanmo Yang,and Young-Ho Kim ACM CHI 2024 (PDF)
MindfulDiary: Harnessing Large Language Model to Support Psychiatric Patients' Journaling Taewan Kim, Seolyeong Bae, Hyun Ah Kim, Su-woo Lee, Hwajung Hong, Chanmo Yang*, and Young-Ho Kim*(*co-corresponding) ACM CHI 2024 (PDF)
Understanding the Impact of Long-Term Memory on Self-Disclosure with Large Language Model-Driven Chatbots for Public Health Intervention Eunkyung Jo, Yuin Jeong, SoHyun Park, Daniel A. Epstein, and Young-Ho Kim ACM CHI 2024 (PDF)
DiaryMate: Understanding User Perceptions and Experience in Human-AI Collaboration for Personal Journaling Taewan Kim, Donghoon Shin, Young-Ho Kim, and Hwajung Hong ACM CHI 2024 (PDF)
GenQuery: Supporting Expressive Visual Search with Generative Models Kihoon Son, DaEun Choi, Tae Soo Kim, Young-Ho Kim, and Juho Kim ACM CHI 2024 (PDF)
EvalLM: Interactive Evaluation of Large Language Model Prompts on User-Defined Criteria Tae Soo Kim, Yoonjoo Lee, Jamin Shin, Young-Ho Kim, and Juho Kim ACM CHI 2024 (PDF)
Leveraging Large Language Models to Power Chatbots for Collecting User Self-Reported Data Jing Wei, Sungdong Kim, Hyunhoon Jung, and Young-Ho Kim PACM HCI (CSCW 2024)
2023
The Bot on Speaking Terms: The Effects of Conversation Architecture on Perceptions of Conversational Agents Christina Wei, Young-Ho Kim, and Anastasia Kuzminykh ACM CUI 2023 (PDF)
Designing a Direct Feedback Loop between Humans and Convolutional Neural Networks through Local Explanations Tong Sun, Yuyang Gao, Shubham Khaladkar, Sijia Liu, Liang Zhao, Young-Ho Kim, and Sungsoo Ray Hong PACM HCI (CSCW 2023) (PDF)
[CHI Best Paper Award] Understanding the Benefits and Challenges of Deploying Conversational AI Leveraging Large Language Models for Public Health Intervention Eunkyung Jo, Daniel A. Epstein, Hyunhoon Jung, and Young-Ho Kim ACM CHI 2023 (PDF)
AVscript: Accessible Video Editing with Audio-Visual Scripts Mina Huh, Saelyne Yang, Yi-Hao Peng, Xiang 'Anthony' Chen, Young-Ho Kim, and Amy Pavel ACM CHI 2023 (PDF)
DataHalo: A Customizable Notification Visualization System for Personalized and Longitudinal Interactions Guhyun Han, Jaehun Jung, Young-Ho Kim*, and Jinwook Seo* (*co-corresponding) ACM CHI 2023 (PDF)
DAPIE: Interactive Step-by-Step Explanatory Dialogues to Answer Children’s Why and How Questions Yoonjoo Lee, Tae Soo Kim, Sungdong Kim, Yohan Yun, Juho Kim ACM CHI 2023
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