I am a PhD student at Korea Advanced Institute of Science and Technology (KAIST) advised by Professor Jaegul Choo. I am mainly interested in robustness of AI, previously focused on debiasing and addressing domain shifts in image classification.
[Apr 2023] I started my regular position as a machine learning research engineer at Qualcomm Korea.
[Apr 2023] I finished my internship at Qualcomm Korea.
[Oct 2022] I started my internship at Qualcomm Korea.
[Aug 2022] I finished my internship at AI Lab, Kakao Enterprise.
[Aug 2021] I started my internship at AI Lab, Kakao Enterprise.
[July 2023] Our "Wisdom of Crowds" paper was accepted at ICCV 2023.
[July 2023] Our "CAFA" paper was accepted at ICCV 2023.
[June 2023] Our "ReLD" paper was accepted at ECML/PKDD 2023.
[Feb 2023] Our "EcoTTA" paper was accepted at CVPR 2023.
[Nov 2022] Our "BiasEnsemble" paper was accepted as oral presentation at AAAI 2023.
[April 2022] Our "DASH" paper was accepted at EuroVis 2022 (Short paper).
[Oct 2021] Our "Inter-Prototype (Face)" paper was accepted at BMVC 2021.
[Sep 2021] Our "Debiasing" paper was accepted as oral presentation at NeurIPS 2021.
[July 2021] Our "SML" paper was accepted as oral presentation at ICCV 2021.
Towards Open-set Test-Time Adaptation Utilizing the Wisdom of Crowds in Entropy Minimization.
Jungsoo Lee, Debasmit Das, Jaegul Choo, and Sungha Choi.
CAFA: Class-Aware Feature Alignment for Test-Time Adaptation
Sanghun Jung, Jungsoo Lee, Nanhee Kim, Amirreza Shaban, Byron Boots, and Jaegul Choo
Deep Imbalanced Time-series Forecasting via Local Discrepancy Density.
Junwoo Park, Jungsoo Lee, Youngin Cho, Woncheol Shin, Dongmin Kim, Jaegul Choo, and Edward Choi.
EcoTTA: Memory-Efficient Continual Test-time Adaptation via Self-distilled Regularization.
Junha Song, Jungsoo Lee, In So Kweon, and Sungha Choi.
DASH: Visual Analytics for Debiasing Image Classification via User-Driven Synthetic Data Augmentation.
Bum Chul Kwon, Jungsoo Lee, Chaeyeon Chung, Nyoungwoo Lee, Ho-jin Choi, and Jaegul Choo.
EuroVis 2022 (Short paper)
Love in Lyrics: An Exploration of Supporting Textual Manifestation of Affection in Social Messaging
Taewook Kim, Jungsoo Lee, Zhenhui Peng, and Xiaojuan Ma