Published

• Kim, S., Cha, J., Kim, D., & Park, E.* (2023). Understanding mental health issues in different subdomains in social networking services: computational analysis of text-based reddit posts. Journal of medical Internet research (SCIE, JCR 2023 IF=7.4, Q1 in Medical Informatics, Health Care Sciences and Services).

• Kim, S., An, C., Cha, J., Kim, D., & Park, E. (2023). D-ViSA: A Dataset for Detecting Visual Sentiment from Art Images. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 3051-3059).  (ICCV; Conference and Proceedings, IS=40.60, SJR=5.662, H Index=313)

• Cha, J., Kim, S., & Park, E.* (2022). A lexicon-based approach to examine depression detection in social media: The case of Twitter and university community. Humanities & Social Sciences Communications, 9(325), 1-10 (SSCI, A&HCI, JCR 2021 IF=2.731, Q2 in Social Sciences, Interdisciplinary).

• Lee, S., Kim, J., Choi, E. B., Shin, S., Kim, D., Yu, H., Kim, S., Na, W. S., & Park, E.* (2022). Computational analysis of a collaboration network on human-computer interaction in Korea. Mathematical Biosciences and Engineering, 19(12), 13911–13927 (SCIE, JCR 2021 IF=2.194).

• Jeong, D., Yu, H., Cha, J., Kim, S., Ahn, H., Chi, Y., Lee, G., Noh, M., & Park, E.* (2022). A Study on Building Ground Penetrating Radar Data and Artificial Intelligence-based Cavity Detection (지표 투과 레이더 탐사 데이터 구축과 인공지능 기반 지하 공동 탐지에 대한 연구). In Proceedings of Korea Software Congress 2022 (KSC '22, Korean, pp. 300-302).

Accepted

• Kim, S., Yu, H., Yoon, J., & Park, E.* (accepted). Micro-Locational Fine Dust Prediction Utilizing Machine Learning and Deep Learning Models. Computer Systems Science and Engineering (SCIE, JCR 2022 IF=4.397, Q1 in Computer Science, Hardware & Architecture).

• Lee, S., Kim, S., Chu, Y., Choi, J., Park, E.* & Woo, S. S. (accepted). EAE-GAN: Emotion-Aware Emoji Generative Adversarial Network for Computationally Modeling Diverse and Fine-Grained Human Emotions. IEEE Transactions on Computational Social Systems (SCIE, JCR 2022 IF=5.0, Q1 in Computer Science, Cybernetics).

Under review

• Lee, S., Kim, S., & Park, E.* (under review). Enhancing Dimensional Image Emotion Detection with a Low-resource Dataset via Two-stage Training.

• Cha, J., Kim, S., & Park, E.* (under review). MOGAM: A Multimodal Object-oriented Graph Attention Model for depression detection.

• An Emoji Generaition System that Expresses User Emotions Based on Conditional Adversarial Generative Network (조건부 적대적 생성 신경망을 기반으로 사용자의 감정을 표현하는 이모지 생성 시스템), KR-Application No. 10-2023-0040064, 2023-03-27.

• Method for Detecting High-Risk Groups for Depression Based on User Vlog Video Data (사용자 브이로그 동영상 데이터 기반의 우울증 고위험군 탐지 방법), KR-Application No. 10-2023-0006850, 2023-01-17.

• [Aug. 2022~Dec. 2022] Teaching Assistant @ Basics for Machine Learning 

• [Dec. 2021 ~ Feb. 2022] Internship @ Data Science Team , Dacon, Inc. 

• [Dec. 2020 ~ Mar. 2021] Award 3rd prize @ English Edu-tech Start-up PM Contest , RingleEnglishEducationService, Inc.

• [Sep. 2020 ~ Feb. 2021] Categorical Data Analysis Team @ Statistical Analytic Academy (P-Sat), Department of Statistics of SKKU, Korea

English (Advanced)

• OPIc AL

• TOEIC 970 (LC 485 / RC 485)

French (Beginner)

• DELF A2

• [SKKU AI Colloquium 2023] 김서윤-D-ViSA: A Dataset for Detecting Visual Sentiment from Art Images

• ASI @ ICCV 2023 spotlight papers