Hyeongsub Kim
AI Research Scientist @LG CNS | Ph.D. candidate @SNU AIBL lab
Contact: E13, LG Science Park, 71, Magokjungang 8-ro, Gangseo-gu, Seoul, South Korea
Email: hyeongsub.kim@snu.ac.kr
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Greetings!
My name is Hyeongsub Kim, and I am currently an AI Research Scientist at LG CNS in South Korea, where I work as a member of the GenAI Strategy & Alliance Team. In this role, I explore the practical deployment of agentic AI within complex enterprise ecosystems, focusing on identifying scalable applications that drive operational efficiency and deliver measurable business impact.
I am honored to have been admitted to the Ph.D. program at the Graduate School of Artificial Intelligence at Seoul National University, where I pursue research under the guidance of Professor Kyungsu Kim. My academic interests center on advancing Core AI research, particularly in developing foundational methodologies that generalize across domains. Within the medical AI domain, I am especially interested in gene expression prediction from H&E-stained histopathology images and the integration of multi-modal biomedical data to uncover deeper biological insights. I aspire to bridge theoretical innovation with practical implementation, translating cutting-edge research into deployable solutions that maximize the societal and industrial value of AI.
My earlier research has primarily focused on the medical AI domain, with an emphasis on computational pathology and bio-signal processing. These projects have cultivated a deep understanding of multi-modal data analysis, which I view as essential for building robust, generalizable AI systems. Additionally, my work on a deep learning-based steel scrap classification system for Daehan Steel, a leading Korean steel manufacturer, broadened my perspective on applying AI beyond healthcare and into industrial contexts.
Building on this foundation, my goal is to contribute to the development of generalizable AI methodologies that unify insights across diverse data modalities, with a particular focus on advancing AI-driven biomedical research such as spatially informed gene expression modeling, thereby expanding the frontiers of both academic research and real-world applications.
News
News
[Sep 2025] I started my Ph.D. studies at the Graduate School of Artificial Intelligence at Seoul National University.
[May 2025] I joined the GenAI Strategy/Alliance Team, where I am actively participating in AI transformation initiatives across internal enterprise systems.
[Jan 2025] Received the 2024 Division Director Award at LG CNS for outstanding contributions to the development of innovative AI solutions, driving impactful business outcomes.
[Oct 2023] Paper on early prediction of respiratory failure in the neonatal intensive care unit using electronic health records has been accepted to BMC pediatrics.
[Sep 2023] I joined LG CNS Multi-modal AX Business Team as an AI research scientist.
[Oct 2022] I joined VUNO Bio-signal Team as an AI research scientist.
[Sep 2022] Paper on medical image learning with limited and noisy data has been accepted to MICCAI 2022 workshop as a poster presentation.
[Nov 2021] Paper on compressed domain segmentation has been accepted to Scientific Report.
[Aug 2021] I joined VUNO Digital Oncology Team as an AI research scientist.
Publications
Publications
Early Prediction of Need for Invasive Mechanical Ventilation in the Neonatal Intensive Care Unit using Artificial Intelligence and Electronic Health Records: A Clinical Study
Young A Kim†, Hyeongsub Kim†, Jaewoo Choi, Kyungjae Cho, Dongjoon Yoo, Yeha Lee, Su Jeong Park, Mun Hui Jeaong, Seong Hee Jeong, Kyung Hee Park, Shin-Yun Byun, Taehwa Kim, Sung-Ho Ahn, Woo Hyun Cho, Narae Lee.
In BMC Pediatrics 23.1 (2023): 525.
Deep learning-based computed tomographic image super-resolution via wavelet embedding
Hyeongsub Kim, Haenghwa Lee, Donghoon Lee.
In Radiation Physics and Chemistry 205 (2023): 110718.
Abstraction in Pixel-wise Noisy Annotation Can Guide Attention to Improve Prostate Cancer Assessment
Hyeongsub Kim, Seo Taek Kong, Hongseok Lee, Kyungdoc kim, Kyu-Hwan Jung.
In MICCAI workshop, 2022 poster presentation.
Deep learning-based histopathological segmentation for whole slide images of colorectal cancer in a compressed domain
Hyeongsub Kim, Hongjoon Yoon, Nishant Thakur, Gyoyeon Hwang, Eun Jung Lee, Chulhong Kim, Yosep Chong.
In Scientific Report 11 (1), 22520.
GPU-accelerated 3D volumetric X-ray-induced acoustic computed tomography
Donghyun Lee, Eun-Yeong Park, Seongwook Choi, Hyeongsub Kim, Jung-joon Min, Changho Lee, Chulhong Kim.
In Biomedical optics express 11 (2), 752-761.
Education
Education
Sep 2025 - Present
Seoul National University, South Korea
Ph.D. Candidate in Graduate School of Artificial Intelligence
Mar 2018 - Feb 2021
Pohang University of Science and Technology (POSTECH), South Korea
M.S. in School of Interdisciplinary Bioscience and Bioengineering
Mar 2009 - Feb 2018
Yonsei University, South Korea
B.S. in Radiological Science and Electrical Engineering (double major)
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