Sungjoon Park

Sungjoon Park

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Bio

I am an AI applied scientist at Healingpaper (Gangnam Unni) and a visiting professor at the Medical Science Research Institute, Kyung Hee University Medical Center. My research sits at the intersection of psychiatry and AI, with a current focus on AI-based suicide prevention education and AI agents for psychiatric care. I also serve as an advisory member of Korea's National AI Strategy Committee and chair the AI Suicide Prevention Strategy Committee of the Korea Association for Suicide Prevention.

Previously, I co-founded SoftlyAI and served as its CEO. I received my Ph.D. from the School of Computing at KAIST, advised by Alice Oh, with support from the Google PhD Fellowship, and my B.S. and M.S. in Psychology from Seoul National University.

Research Interest

I am interested in psychiatric research, especially in how AI can bring psychiatric care to people who would otherwise go without it. My earlier work developed machine learning models that measure psychological characteristics from language and applied them to computational psychotherapy. Building on this foundation, I currently focus on three directions.

  • AI-based suicide prevention education. People at risk of suicide often reveal their distress to family, friends, or coworkers before they ever reach a clinician. I develop AI supervisors that help these gatekeepers, as well as social workers and other frontline staff, practice standardized suicide prevention training: noticing warning signs, asking directly about suicide, and connecting the person to professional help. The AI's feedback is grounded in a knowledge base built from psychiatrists' expert review.
  • Environments for psychiatric AI agents. Static question-answering benchmarks reveal little about how an AI agent will behave over the course of real clinical care. I design environments in which agents interview simulated patients over multiple sessions, use clinical tools, and are assessed against expert-verified standards for diagnostic judgment, treatment planning, and safety-critical responses. A central challenge is building simulated patients that stay clinically consistent across sessions and, as a population, reflect real-world symptom distributions. These environments are meant both to evaluate psychiatric agents and to train them.
  • AI-based psychotherapy applications. Psychiatry has spent more than a century refining psychotherapy and medication into effective treatments, yet most people who need them do not receive them, held back by cost, stigma, and a shortage of clinicians. I envision a future in which anyone can receive a full course of personalized psychiatric care, from psychotherapy to medication and aftercare, privately, whenever and wherever they need it, without depending on a hospital. I am working toward that future by building AI psychotherapy applications grounded in clinical practice rather than generic wellness features, with the long-term goal of delivering therapy that matches, and eventually surpasses, the care of the best human experts.

Education

  • KAIST, Ph.D, Computer Science, Mar 2016 – Feb 2022
  • Seoul National University, M.S., Quantitative Psychology, Mar 2012 – Aug 2014
  • Seoul National University, B.S., Psychology, Mar 2007 – Feb 2012

Work Experience

Current

  • AI Applied Scientist, Healingpaper (Gangnam Unni) (July 2025 – Present)
  • Visiting Professor, Medical Science Research Institute, Kyung Hee University Medical Center (May 2026 – Present)
  • Advisory Member, National AI Strategy Committee (Oct 2025 – Present)
  • Chair, AI Suicide Prevention Strategy Committee, Korea Association for Suicide Prevention (Mar 2026 – Present)

Past

  • CEO / co-founder, SoftlyAI (Jan 2022 – July 2025)
  • Research Engineer, Upstage (Oct 2020 – Oct 2021)
  • Research Intern, Google Research (July 2020 – Oct 2020)
  • Research Intern, KakaoBrain (Mar 2020 – July 2020)
  • Research Intern, Clova AI Research, Naver (Aug 2019 – Feb 2020)
  • Graduate Researcher, KAIST, U&I Lab (Dec 2014 – Feb 2016)
  • Research Assistant, SNU Asia Center (Jan 2014 – Aug 2014)

Awards

  • PhD Dissertation Award, Dept. of Computing, KAIST
  • Google Ph.D Fellowship, Natural Language Processing, Sep 2019 - Aug 2020

Publications

  1. Park, K., Baik, M.J., Hwang, Y.J., Shin, Y., Lee, H.J., Lee, R., Lee, S.M., Sun, J.Y.H., Lee, A.R., Yoon, S.Y., Lee, D.H., Moon, J., Bak, J., Cho, K., Paik, J.W., Park, S. (2026) Iterative Large Language Model-Guided Sampling and Expert-Annotated Benchmark Corpus for Harmful Suicide Content Detection: Development and Validation Study JMIR Medical Informatics, 14, e73725
    Paper PubMed Arxiv
  2. Lee, D.H., Cho, H., Jin, W., Moon, J., Park, S., Röttger, P., Pujara, J., Lee, R.K.W. (2024) Improving Covert Toxicity Detection by Retrieving and Generating References In Proceedings of the 8th Workshop on Online Abuse and Harms (WOAH 2024)
    PDF
  3. Shin, J., Yoon, H., Lee, S., Park, S., Liu, Y., Choi, J.D., Lee, S.J. (2023) FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learning In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023)
    PDF
  4. Moon, J., Lee, D.H., Cho, H., Jin, W., Park, C.Y., Kim, M., May, J., Pujara, J., Park, S. (2023) Analyzing Norm Violations in Live-Stream Chat In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023)
    PDF
  5. Yoon, S., Park, S., Kim, G., Cho, J., Park, K., Kim, G., Seo, M., Oh, A. (2023) Towards Standardizing Korean Grammatical Error Correction: Datasets and Annotation In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL 2023)
    PDF
  6. Jeong, Y., Oh, J., Lee, J., Ahn, J., Moon, J., Park, S., Oh, A. (2022) KOLD: Korean Offensive Language Dataset In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022)
    PDF
  7. Park, S.*, Moon, J.*, Kim, S.*, Cho, W.*, Han, J., Park, J., Song, C., Kim, J., Song, Y., Oh, T., Lee, J., Oh, J., Lyu, S., Jeong, Y., Lee, I., Seo, S., Lee, D., Kim, H., Lee, M., Jang, S., Do, S., Kim, S., Lim, K., Lee, J., Park, K., Shin, J., Kim, S., Park, L., Oh, A., Ha, J., Cho, K. (2021) KLUE: Korean Language Understanding Evaluation In NeurIPS 2021 Datasets and Benchmarks Track (Neurips 2021) *equal contribution
    Neurips Arxiv Github Leaderboard
  8. Park, S., Kim, J., Ye, S., Jeon, J., Park, H., Oh, A. (2021) Dimensional Emotion Detection in Categorical Emotion In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP 2021)
    PDF Data
  9. Park, S.*, Park, K.*, Ahn, J. Oh, A. (2020) Suicidal Risk Detection for Military Personnel In Proceedings of the in the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020) *equal contribution
    PDF Data
  10. Park, C., Shin, J., Park, S., Lim, J., Lee, C. (2019) Fast End-to-end Coreference Resolution for Korean In Proceedings of the Findings of 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020 - Findings)
    PDF
  11. Seonwoo, Y., Park, S., Kim, D., Oh, A. (2019) Additive Compositionality of Word Vectors In Workshop on Noisy User-generated Text (W-NUT) @EMNLP 2019
    PDF
  12. Park, S., Park, H., Kim, C. (2019) A Comparison between Factor Structure and Semantic Representation of Personality Test Items Using Latent Semantic Analysis Korean Journal of Cognitive Science, 30(3)
    PDF
  13. Park, S., Kim, D., Oh, A. (2019) Conversation Model Fine-Tuning for Classifying Client Utterances in Counseling Dialogues In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL 2019)
    PDF
  14. Park, S., Seonwoo, Y., Kim, J., Kim, J., Oh, A. (2019) Denoising Recurrent Neural Networks for Classifying Crash-related Events IEEE Transactions of Intelligent Transportation Systems.
    PDF
  15. Seonwoo, Y., Park, S., Oh, A. (2018) Hierarchical Dirichlet Gaussian Marked Hawkes Process for Narrative Reconstruction in Continuous Time Domain In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP 2018)
    PDF
  16. Park, S., Byun, J., Baek, S., Cho, Y., Oh, A. (2018) Subword-level Word Vector Representations for Korean In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018)
    PDF Data
  17. Park, S., Bak, J., Oh, A. (2017) Rotated Word Vector Representations and their Interpretability. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2017)
    PDF Source
  18. Kim, S., Park, S., Hale, S. A., Kim, S., Byun, J., & Oh, A. H. (2016). Understanding editing behaviors in multilingual Wikipedia. PLOS ONE, 11(5), e0155305.
    Article
  19. Kim, J., Keegan, B. C., Park, S., & Oh, A. (2016). The Proficiency-Congruency Dilemma: Virtual Team Design and Performance in Multiplayer Online Games. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI 2016)
    PDF
  20. Park, S., Kim, S., Hale, S. A., Kim, S., Byun, J., & Oh, A. (2015). Multilingual Wikipedia: Editors of Primary Language Contribute to More Complex Articles. In Ninth International AAAI Conference on Web and Social Media.(Wiki-ICWSM Workshop in ICWCM 2015)
    PDF

Talks / Presentations

  • 2020.7.30 Conversation Model Fine-Tuning for Classifying Client Utterances in Counseling Dialogues, Google Ph.D Fellowship Summit.
  • 2018.9.10 Subword-level Word Vector Representations for Korean & Learning Client's Information from Counseling Conversations, Facebook AI Research, Paris.
  • 2017.10.19. Rotated Word Vector Representations and their Interpretability (Poster), Samsung AI Forum 2017.
  • 2016.11.18 Chung, M., Park, S., Kim, M., & Harris, C. R. Medical Embarrassment: A Cross-cultural Perspective. Poster presented at the Psychonomics 2016, Boston, MA.

Teaching Experiences

  • SEP592 Special Topics in Software (Introduction to Data Science), KAIST, Head TA, (Spring, 2019)
  • CS570 Artificial Intelligence & Machine Learning, KAIST, Head TA, (Spring, 2017)
  • CS206 Data Structure, KAIST, TA, (Fall, 2016)
  • Advanced Psychological Statistics, Seoul National University, TA, (Spring, 2013)
  • Psychological Statistics, Seoul National University, TA, (Spring, 2012)

Academic Services

  • Reviewer, EMNLP 2022, EMNLP 2020, EMNLP 2019, ACL 2019
  • Invited Reviewer, WWW 2019
  • Subreviewer, EACL 2017

References

  • Prof. Alice Haeyun Oh, Department of Computing, KAIST, alice.oh@kaist.edu