(Last update: 26.03)
Personal: yoonjeewoo@gmail.com
Business: jeewooyoon@raondata.ai
Bio
Jeewoo Yoon is an AI technical leader and researcher dedicated to bridging the gap between state-of-the-art academic research and scalable industrial applications. Currently serving as the CTO at Raondata, he leads the development of innovative AI solutions, notably 'LIGHTN' for Voice of Customer (VOC) analysis and 'AlphaRetina' for medical computer-aided diagnosis (CAD).
He received his Ph.D. in applied artificial intelligence from Sungkyunkwan University (SKKU). His core expertise lies in multimodal machine learning, affective computing, and natural language processing. With over 20 peer-reviewed publications in top-tier journals and conferences (e.g., AAAI, CIKM, JMIR, Scientific Reports) and multiple patents in AI systems, Jeewoo leverages his strong academic foundation to solve real-world business challenges and drive technological innovation.
Work Experience & Leadership
Raondata | Seoul, South Korea
Chief Technology Officer (CTO) (23.03 ~ Present)
Define and drive the company's overall technical strategy, product roadmap, and R&D direction.
Lead engineering and research initiatives across a multidisciplinary team of 10 professionals, including AI engineers, researchers, medical doctors, and nurses, ensuring seamless cross-functional collaboration.
Secure and manage over 1 billion KRW in major government R&D grants (e.g., IITP, NIPA) to fuel technological innovation, actively building and mentoring a core AI and engineering team to execute these strategic initiatives.
Architect and commercialize 'LIGHTN', a next-generation VOC analysis platform powered by domain-specific sLLMs and a Multi-Agent System. Engineered robust AI pipelines integrating high-accuracy STT and multimodal emotion recognition to accurately decode complex customer interactions.
Manage the full lifecycle development of 'AlphaRetina', a deep learning-based computer-aided diagnosis (CAD) system for retinal diseases using optical coherence tomography (OCT) data, working closely with clinical experts to ensure high medical accuracy and usability.
Founding Member & AI Lead (21.04 ~ 23.02)
Directed a core team of 8 researchers, engineers, and designers to build initial AI models and prototypes.
Architected and deployed highly scalable, production-ready AI models across diverse domains—including TTS, Voice Conversion, STT, Emotion Recognition, and Image Segmentation—overseeing the end-to-end ML lifecycle from core algorithm design to real-world implementation.
Focused on the effective translation of academic AI technologies into viable industry solutions, successfully securing early-stage R&D grants and startup partnerships.
Data Science & Artificial Intelligence Lab @ SKKU | Seoul, South Korea
Lab Leader (21.09 ~ 22.08)
Spearheaded daily lab operations and mentored junior researchers, streamlining research workflows and managing multiple government grant proposals and milestone deliverables.
Cultivated a collaborative research environment, actively guiding junior members in their algorithm design and academic writing, contributing to the lab's overall high publication output.
Researcher (19.11 ~ 23.02)
Collaborated within an interactive group of professors, researchers, and industrial experts to solve complex AI challenges.
Actively contributed to over 10 government-funded research projects (NRF, MSIT, etc.).
Published over 25 AI research papers, with a primary focus on multimodal machine learning and data science.
Education
Ph.D. in Applied Artificial Intelligence at Sungkyunkwan University (19.03 ~ 23.02)
Dissertation: Multimodal Deep Learning in Affective Computing
Advisor: Dr. Jinyoung Han
B.E. in Computer Science & Engineering at Hanyang University (14.03 ~ 19.02)
Selected Publications (full list)
(* = (co-)corresponding author, ** = equal contributions)
Multimodal Affective Computing
Juho Jung, Chaewon Kang, Jeewoo Yoon, Seungbae Kim, Jinyoung Han*, "HiQuE: Hierarchical Question Embedding Network for Multimodal Depression Detection," ACM International Conference on Information and Knowledge Management (CIKM), accepted. (acceptance ratio = 347/1,496= 23.1%)
Kyungeun Min**, Jeewoo Yoon**, Migyeong Kang, Daeun Lee, Eunil Park, and Jinyoung Han*, "Detecting depression on video logs using audiovisual features," Humanities and Social Sciences Communications, 10, 788, Nov, 2023. (SSCI, JCR 2022 IF = 3.5)
Jeewoo Yoon, Jinyoung Han, Erik Bucy, and Jungseock Joo, "Predicting Emotional Intensity in Political Debates via Non-verbal Signals," INTERSPEECH, Sep, 2022.
Jeewoo Yoon, Chaewon Kang, Seungbae Kim, and Jinyoung Han*, "D-Vlog: Multimodal Vlog Dataset for Depression Detection," AAAI Conference on Artificial Intelligence (AAAI), Feb, 2022. (acceptance ratio = 1,345/9,251 = 14.5%)
Medical Computing
Jeewoo Yoon**, Taenyun Kim**, Jinyoung Han**, Joon Seo Hwang, Jeong Mo Han, Ji In Park, Hayeon Song, Daniel Duck-Jin Hwang*, "Expertise Matters in AI Adoption: A Comparative Study of Retina Specialists and General Ophthalmologists in AI-CAD Adoption," International Journal of Human–Computer Interaction (SSCI, JCR 2024 IF = 3.9)
Jeewoo Yoon**, Jinyoung Han**, Junseo Ko, Seong Choi, Ji In Park, Joon Seo Hwang, Jeong Mo Han, Daniel Duck-Jin Hwang*, "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 (JMIR), 25: e48142. (SCIE, JCR 2022 IF=7.4)
Jeewoo Yoon**, Jinyoung Han**, Junseo Ko, Seong Choi, Ji In Park, Joon Seo Hwang, Jeong Mo Han, Kyuhwan Jang, Joonhong Sohn, Kyu Hyung Park, and Daniel Duck-Jin Hwang*, "Classifying central serous chorioretinopathy subtypes with a deep neural network using optical coherence tomography images: A cross-sectional study," Scientific Reports 12, 422(2022), Jan, 2022. (SCI, JCR 2021 IF = 4.996)
Jeewoo Yoon**, Jinyoung Han**, Ji In Park, Joon Seo Hwang, Jeong Mo Han, Joonhong Sohn, Kyu Hyung Park, and Daniel Duck-Jin Hwang*, "Optical Coherence Tomography-based Deep-learning Model for Detecting Central Serous Chorioretinopathy," Scientific Reports 10, 18852(2020), November 2020. (SCI, JCR 2020 IF = 4.379)
Patents
Applied AI Systems
Multi-modal Based Low Quality Landline Specialized Emotion Recognition System for Artificial Intelligence Contact Center (App. No: 2025-02-19-1020250021365)
System for Providing Online Commerce Review Analysis Service (App. No: 2024-09-13-1020240125509)
System for Providing Voice of Customer Total Management Service for City Gas Company (App. No: 2024-07-23-1020240097141)
Video Segmentation and Content Classification Automation Apparatus (App. No: 2024-02-22-1020240025642)
Artificial Intelligence-based Hair Loss Management Apparatus and Method (App No. 2022-03-22-1020220035362)
Honors & Awards
Global & National Recognitions
AAAI-22 Scholarship, Amazon Science (2022)
1st & 3rd Prize, AI Championship Grand Finals (Automatic Video Indexing), Ministry of SMEs and Startups (2021) [Video]
2nd Prize, Public-Private Partnership - Open Innovation (Multimodal Emotion Recognition), Ministry of SMEs and Startups (2023) [News]
3rd Prize, NIPA Online AI Competition (Speaker Recognition), Ministry of Science and ICT (2022)
2nd Prize, Fake-EmoReact Challenge (Fake News Detection), SocialNLP @ NAACL (2021) [News]
Academic Awards
Best Student Researcher Award (1st & 2nd Prizes), Sungkyunkwan University (Awarded consecutively in 2021, 2022, 2023)
2nd Prize, Capstone Design, Hanyang University (2018) [Video]
R&D Grants & Projects
(* = principal investigator)
[IITP] Advanced and Proactive AI Platform Research and Development Against Malicious Deepfakes (23.04 ~ 25.12)
[NIPA] Developing an AI-based Video Segmentation and Content Classification System (21.07 ~ 24.06)
[MSS] Developing a TTS Solution for Interactive Smart Toy Service for Infants and Toddlers (23.04 ~ 23.10)
[NRF] Developing a Multimodal Deep Learning Model for Understanding Videos toward Depression Detection* (22.06 ~ 24.06)
[ETRI] Developing a Multimodal Deep Learning Model for Depression Detection on Video Data (21.09 ~ 21.11)
[NRF] Developing Artificial Intelligence Models and Korean Datasets for Detecting Suicide Risk (22.05 ~ 24.04)
[MSIT] Study on Self-Driving B5G Networks towards Federated Private-5G (21.06 ~ 24.02)
[NRF] Modeling and Analyzing a Contact Social Graph for Predicting and Managing Outbreak Disease (20.10 ~ 23.09)
[NRF] Predicting Information Diffusion Process with Deep Learning (18.11 ~ 21.10)
[ETRI] An Artificial Intelligence System for Addressing Social Issues (21.05 ~ 21.08)
[ETRI] Developing a Model for Detecting Fake News on COVID-19 (20.07 ~ 20.11)
[ETRI] Learning and Predicting Information Diffusion (19.06 ~ 19.11)
[ETRI] Big Data Research for Artificial Intelligence Applications (18.05 ~ 18.11)
Talks & Teaching
Invited Talks
2022 SKKU AI Colloquium: "D-Vlog: Multimodal Vlog Dataset for Depression Detection" [Video]
72nd Annual ICA Conference: "Non-verbal Emotional Intensity of Candidates in Debate Videos Predicts Favorability Polls" (Video-as-data in computational communication panel) [Video]
Teaching Experience
Teaching Fellow at Sungkyunkwan Univ. & Hanyang Univ. (2018 – 2022)
Conducted lectures and sessions for core CS and AI courses, including Advanced Machine Learning / Deep Learning, Data Mining, Algorithms, and System Programming.
Media