Upload Records Snowball Search Search OpenAlex
About the database ScholarIQanswers from OpenAlex & ORCID
How has Haechang Kim's publication output changed over time?
ScholarIQpublication output · 2024–2026
Output grew100% over the shown period — from 1 works in 2024 to 2 in 2026.
1
2
2
202420252026
What are the most-cited papers on Haechang Kim?
ScholarIQmost cited works
Accelerated Structural Optimization for the Supported Metal System Based on Hybrid Approach Combining Bayesian Optimization with Local Search
Shinyoung Bae, Dongjae Shin, Haechang Kim, Jeong Woo Han, Jong Min Lee
S189701308. 20243 Citations
TalkToAgent: A multi-agent LLM Framework for natural language explanation of reinforcement learning policies
Haechang Kim, Hao Chen, Can Li, Jong Min Lee
Computers & Chemical Engineering. 20261 Citations
Decoding industrial-scale battery manufacturing process through integration of causal graphs into explainable artificial intelligence
Haechang Kim, Ji Young Yun, Jung Eunjoo, Bora Lee, Bora Lee, Hyeongseok Kim, Jong Min Lee
Engineering Applications of Artificial Intelligence. 20250 Citations
Image-based Battery Health Monitoring for Capacity Degradation Analysis
Ji Young Yun, Haechang Kim, Jong Min Lee
IFAC-PapersOnLine. 20250 CitationsOPEN ACCESS
Degradation-Aware Self-Supervised Learning with Interpretable Battery-State Representations for Label-Efficient Battery Health Diagnostics
Ji Young Yun, Haechang Kim, Jong Min Lee
SSRN Electronic Journal. 20260 CitationsOPEN ACCESS
Related on ScholarIQ
Seoul National University
Institution
Accelerated Structural Optimization for the Supported Metal System Based on Hybrid Approach Combining Bayesian Optimization with Local Search
Paper
TalkToAgent: A multi-agent LLM Framework for natural language explanation of reinforcement learning policies
Paper
Decoding industrial-scale battery manufacturing process through integration of causal graphs into explainable artificial intelligence
Paper
Image-based Battery Health Monitoring for Capacity Degradation Analysis
Paper
Degradation-Aware Self-Supervised Learning with Interpretable Battery-State Representations for Label-Efficient Battery Health Diagnostics
Paper