The Data Analytics Lab conducts research on novel statistical and data science methodologies that leverage industrial statistics and DL/ML to solve complex engineering problems arising in diverse industrial settings. In particular, we focus on practical problem-solving in quality engineering, with an emphasis on quality improvement, system monitoring and anomaly detection, and time-series representation learning in the manufacturing and logistics domains.
UNIST 산업공학과 데이터 분석 연구실은 산업 통계와 딥러닝/기계학습을 기반으로 다양한 산업 현장에서 발생하는 복잡한 공학 문제를 해결하는 새로운 통계 및 데이터 사이언스 방법론을 연구합니다. 특히 제조와 물류 분야에서 발생하는 품질 향상, 시스템 모니터링·이상 탐지, 시계열 표현학습을 중심으로 품질 공학의 실제 문제 해결에 집중하고 있습니다.
A domain knowledge–based data refinement methodology for detecting defective products that cannot be filtered out in the LQC process.
Developing a new metric for measuring customer satisfaction based on user activity data
An ST-based method using failure pattern knowledge to detect tiny anomalies in manufacturing time-series data.
Email: sungil.kim@unist.ac.kr · Address: 유니스트길 50, 112동 302-4
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