I am co-founder and CEO of Softly.ai, developing AI-based automated content moderation solutions. I received Ph.D. from School of Computing at KAIST, advised by Alice Oh. My research was supported by Google PhD Fellowship program. Before that, I obtained BA and MA in Department of Psychology in Seoul National University.
- 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
- CEO / co-founder, SoftlyAI (Jan 2022 – Present)
- 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) Advisor: Alice Oh.
- Research Assistant, SNU Asia Center (Jan 2014 – Aug 2014)
- PhD Dissertation Award, Dept. of Computing, KAIST
- Google Ph.D Fellowship, Natural Language Processing, Sep 2019 - Aug 2020
My research aims to (1) develop machine learning models which can understand complicated psychological characteristics of people from text and (2) apply them to computational psychotherapy applications, in order to lead people live in better mental state. I focus on natural language since is a good indicator of latent psychological states of humans, and this is basically because our daily usage of language encodes important personality characteristics even in a single word, and further it could represent psychological states during conversation.
However, the relationship between language and mental state would be highly complex and difficult to be inferred precisely, thus learning the regularities through carefully designed machine learning models over fine-grained data are natural to be preferred than applying rule-based techniques. Furthermore, once a model can learn the relationship between them, then various psychotherapy programs would be applicable based on the detected mental state, leads to positive psychological changes. Thus my research agenda has developed to three step-by-step subproblems: (a) Learning Representation of Text, (b) Measuring Psychological Characteristics from Text, and (c) Developing Psychotherapy Applications.
Each of the problems is closely related to each other that one could evoke another question: What characteristic should be measured to develop an effective psychotherapy applications? or, what representation learning method should be needed to measure the extent of mental disorders from text? Throughout conducting research to answer these problems, I would like to bridge the gap between two fields: natural language processing and computational psychotherapy.
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
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)
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
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)
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
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), pp.133-156.
Park, S., Kim, D., Oh, A. (2019) Conversation Model Fine-Tuning for Classifying Client Utterances in Counseling Dialogues In Proceedings of the 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2019)
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.
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)
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 Poster Data
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 Poster Source
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.
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)
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)
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.
- 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)
- Reviewer, EMNLP 2022, EMNLP 2020, EMNLP 2019, ACL 2019
- Invited Reviewer, WWW 2019
- Subreviewer, EACL 2017
- Prof. Alice Haeyun Oh, Department of Computing, KAIST, email@example.com
sungjoon.park (at) softly.ai
CEO / co-founder
Seoul, Republic of Korea
(Last Update: 03/19/2022)