Yonsei University Health System (YUHS)
M.S. in Digital Analytics
shiwonkim.sean(at)gmail.com
I am an AI researcher with primary interests in developing scalable and data-efficient learning systems, particularly for applications in medical imaging. My work focuses on learning from evolving and limited (scarce or privacy-sensitive) data conditions that are common in real-world scenarios but remain challenging for deep learning models. Learn more about my research here.
Research keywords include: continual learning, few-shot learning, medical imaging, and so on.
I am currently a research assistant at the Medical Informatics Collaboration Unit (MCU) of Yonsei University Health System (YUHS) in Seoul, Republic of Korea. Previously, I finished my M.S. in Digital Analytics at Yonsei University in August 2025, under the supervision of Prof. Yu Rang Park at the Digital Healthcare Lab (DHLab). I received my B.B.A. in Business Administration from the same university in 2023. From August 2024 to February 2025, I was a visiting scholar at the CMU School of Computer Science as part of the Carnegie Mellon University Intensive AI Education Program sponsored by the South Korean government.
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Research Highlights
Does Prior Data Matter? Exploring Joint Training in the Context of Few-Shot Class-Incremental Learning
Shiwon Kim*, Dongjun Hwang*†, Sungwon Woo*, Rita Singh†
International Conference on Computer Vision (ICCV) 2025 CLVision
[TL;DR] [Paper] [Poster] [Code]
Challenged the assumption of limited access to prior data in few-shot class-incremental learning, and compared joint training with incremental learning to empirically assess the practical impact of full data access on model performance.
# continual learning # few-shot learning
Does Prior Data Matter? Exploring Joint Training in the Context of Few-Shot Class-Incremental Learning
Shiwon Kim*, Dongjun Hwang*†, Sungwon Woo*, Rita Singh†
International Conference on Computer Vision (ICCV) 2025 CLVision
[TL;DR] [Paper] [Poster] [Code]
Challenged the assumption of limited access to prior data in few-shot class-incremental learning, and compared joint training with incremental learning to empirically assess the practical impact of full data access on model performance.
# continual learning # few-shot learning
Debiasing Few-Shot Class-Incremental Learning via Dynamic Feature-Classifier Alignment
Shiwon Kim
Master's Thesis, Yonsei University
Proposed a dynamic hierarchical equiangular tight frame (DH-ETF) classifier that mitigates base-class bias by preserving prior knowledge through a fixed cluster-level ETF, while enabling flexible accommodation of new classes using adaptable class-level ETFs within each cluster.
# continual learning # few-shot learning # neural collapse
Debiasing Few-Shot Class-Incremental Learning via Dynamic Feature-Classifier Alignment
Shiwon Kim
Master's Thesis, Yonsei University
Proposed a dynamic hierarchical equiangular tight frame (DH-ETF) classifier that mitigates base-class bias by preserving prior knowledge through a fixed cluster-level ETF, while enabling flexible accommodation of new classes using adaptable class-level ETFs within each cluster.
# continual learning # few-shot learning # neural collapse
Classification Models for Arthropathy Grades of Multiple Joints Based on Hierarchical Continual Learning
Bong Kyung Jang*, Shiwon Kim*, Jae Yong Yu, JaeSeong Hong, Hee Woo Cho, Hong Seon Lee, Jiwoo Park, Jeesoo Woo, Young Han Lee†, Yu Rang Park†
La Radiologia Medica (IF 2024: 9.7)
[TL;DR] [Paper] [Slides] [Code]
Developed and validated a continual learning framework for arthropathy grade classification scalable across multiple joints, using hierarchically labeled radiographs of the knee, elbow, ankle, shoulder, and hip from three tertiary hospitals.
# medical imaging # continual learning
Classification Models for Arthropathy Grades of Multiple Joints Based on Hierarchical Continual Learning
Bong Kyung Jang*, Shiwon Kim*, Jae Yong Yu, JaeSeong Hong, Hee Woo Cho, Hong Seon Lee, Jiwoo Park, Jeesoo Woo, Young Han Lee†, Yu Rang Park†
La Radiologia Medica (IF 2024: 9.7)
[TL;DR] [Paper] [Slides] [Code]
Developed and validated a continual learning framework for arthropathy grade classification scalable across multiple joints, using hierarchically labeled radiographs of the knee, elbow, ankle, shoulder, and hip from three tertiary hospitals.
# medical imaging # continual learning