AI Research Engineer (Incomming Ph.D. candidate @SNU)
GenAI Strategy/Alliance Team, AI Center
LG CNS, Seoul, South Korea
Contact: E13, LG Science Park, 71, Magokjungang 8-ro, Gangseo-gu, Seoul, South Korea
Email: hyeongsub.kim@snu.ac.kr
Greetings!
My name is Hyeongsub Kim, and I am currently working as an AI Research Engineer at LG CNS in South Korea. I am a member of the GenAI Strategy & Alliance Team, where I am actively involved in AI transformation initiatives across internal enterprise systems. This role has allowed me to explore the practical application of agentic AI and contribute to identifying scalable use cases that enhance operational efficiency within the company.
Previously, I worked on a project with Daehan Steel, a leading steel manufacturer in South Korea, where I developed a deep learning-based steel scrap classification system. This experience helped me broaden my expertise beyond the medical domain, particularly in applying AI to industrial contexts.
My career has predominantly centered around the medical domain. I have conducted research in AI-based medical data analysis—especially pathological image—and bio-signal processing. These experiences have deepened my understanding of multi-modal data analysis. Building on this foundation, I am passionate about advancing research that integrates diverse data modalities, and I am eager to apply my skills to domains with tangible, real-world impact.
[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.
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.
Sep 2025 -
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 i-bio
Mar 2009 - Feb 2018
Yonsei University, South Korea
B.S. in Radiological Science and Electrical Engineering (double major)
