I am a Ph.D candidate at Korea Advanced Institute of Science and Technology (KAIST) advised by Professor Jaegul Choo.

My research focuses on responsible and reliable AI, with a particular emphasis on bias mitigation. I have explored debiasing tasks in classification models and recently expanded my interests to address societal bias in models trained with large-scale datasets, especially diffusion-based text-to-image (T2I) generative models.

Contact

  • jeonghoon_park [at] kaist.ac.kr

  • Bundang-gu, Seongnam-si, Gyeonggi-do, South Korea

Education

  • M.S in Artifical Intelligence, 2020-2022

    KAIST, Korea

    Advisor: Prof. Jaegul Choo

  • B.S in Computer Science, 2015-2020

    Korea University, Korea

Professional Experiences

  • Research Internship at Kakao Enterprise
    Aug 2022 - Nov 2022

    Conducted research on debiasing classification models

Publications & Research Works

  • Disentangling Subject-Irrelevant Elements against Subject in Personalized Text-to-Image Diffusion via Filtered Self-distillation

    Seunghwan Choi, Jooyeol Yun, Jeonghoon Park, and Jaegul Choo

    Winter Conference on Applications of Computer Vision (WACV), 2025

  • Enhancing Intrinsic Features for Debiasing via Investigating Class-Discerning Common Attributes in Bias-Contrastive Pair

    Jeonghoon Park*, Chaeyeon Chung*, Juyoung Lee, Jaegul Choo

    Proceedings of the IEEE/CVF Converence on Computer Vision and Pattern Recognition (CVPR), 2024

    Paper

  • BiasEnsemble: Revisiting the Importance of Amplifying Bias for Debiasing

    Jungsoo Lee*, Jeonghoon Park*, Daeyoung Kim*, Juyoung Lee, Edward Choi, Jaegul Choo

    AAAI Conference on Artificial Intelligence (AAAI), 2023

    Accepted as Oral Presentation (19.6% acceptance rate)

    Paper | Code

  • Training auxiliary prototypical classifiers for explainable anomaly detection in medical image segmentation

    Wonwoo Cho, Jeonghoon Park, Jaegul Choo

    Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023

    Paper

  • Deep Edge-Aware Interactive Colorization against Color-Bleeding Effects

    Eungyeup Kim*, Sanghyeon Lee*, Jeonghoon Park*, Somi Choi, Choonghyun Seo, Jaegul Choo

    Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2021

    Accepted as Oral Presentation (3% acceptance rate)

    Paper | Project Page

  • Natural Attribute-based Shift Detection

    Jeonghoon Park*, Jimin Hong*, Radhika Dua*, Daehoon Gwak, Yixuan Li, Jaegul Choo, Edward Choi

    arXiv, 2021

    Paper

  • Evaluation of Out-of-Distribution Detection Performance of Self-Supervised Learning in a Controllable Environment

    Jeonghoon Park*, Kyungmin Jo*, Daehoon Gwak*, Jimin Hong, Jaegul Choo, Edward Choi

    NeurIPS 2020 Workshop: Self-Supervised Learning - Theory and Practice

    Paper