Faculty Research Profile
Sungil Kim
학력
•Ph.D. in Industrial Engineering, Georgia Tech, 2011
•M.S. in Statistics, Georgia Tech, 2007
•M.S. in Industrial Engineering, Georgia Tech, 2007
•B.S. in Industrial Engineering, Yonsei University, 2005
주요 경력
•Associate Professor, UNIST 2020.9-present
•Assistant Professor, UNIST 2016.7-2020.8
•Senior Engineer, Samsung SDS 2014.1-2016.6
•Consultant, Terra Technology 2011.9-2013.12
수상/학회/외부활동
• Area editor in Statistics, Quality, Reliability & Maintenance, Computers & Industrial Engineering
•Senior Member, INFORMS
•2023 Excellence in Mentoring Award, College of Information and Biotechnology, UNIST
•2021 IISE Best Paper Award, Logistics and Supply Chain Division, The Institute of Industrial and Systems Engineers
•2019 IISE Best Paper Award, Quality Control & Reliability Engineering Division, The Institute of Industrial and Systems Engineers
데이터 애널리틱스 연구실
Data Analytics Lab
다양한 산업 현장에서 발생하는 복잡한 공학 문제를 해결하는 새로운 통계 및 데이터 사이언스 방법론을 연구합니다. 특히, 제조와 물류 분야에서 발생하는 품질 향상, 이상 감지, 시스템 분석 연구를 진행하고 있습니다. 4차 산업혁명 시대에 산업 현장에 인공지능 기술을 접목하여 혁신적인 변화를 이끌어내는 연구에 관심을 가지고 있습니다.
Welcome to the Data Analytics Lab at the Ulsan National Institute of Science and Technology (UNIST). Our research focuses on development of novel statistical methods for solving complex engineering problems. Our team pursues leading-edge research in the field of data science and business analytics with industry, government, and community partners. Our research can be characterized by three aspects: i) statistics as a research methodology, ii) motivation from real data, and iii) applications to industry.
Welcome to the Data Analytics Lab at the Ulsan National Institute of Science and Technology (UNIST). Our research focuses on development of novel statistical methods for solving complex engineering problems. Our team pursues leading-edge research in the field of data science and business analytics with industry, government, and community partners. Our research can be characterized by three aspects: i) statistics as a research methodology, ii) motivation from real data, and iii) applications to industry.
연구분야
System Monitoring & Anomaly Detection, Sequential Learning, Neural Differential Equations, and Uncertainty Quantification
System Monitoring & Anomaly Detection, Sequential Learning, Large-scale Calibration, and Uncertainty Quantification
연구 희망분야
Artificial Intelligence in Quality Engineering
연구주제
Industrial Statistics and Data Analytics; Quality Engineering and Management; Response surface
methodology; Demand forecasting; Machine learning and Data mining; Business Analytics
Industrial Statistics and Data Analytics; Quality Engineering and Management; Response surface
methodology; Demand forecasting; Machine learning and Data mining; Business Analytics
국가연구개발사업 기술 분류체계
국가과학기술표준분류
SC. 경제/경영 > SC09. 생산관리 > SC0904. 품질관리
논문
• YongKyung Oh, Dongyoung Lim, and Sungil Kim (2024), Stable neural stochastic differential equations for irregular time series classification, ICLR (spotlight).
• Jonghwan Mun, Jitae Yoo, Heesun Kim, Nayi Ryu, and Sungil Kim (2024), Domain knowledge-informed functional outlier detection in the refrigerator inspection lanes, Computer & Industrial Engineering, 189, pp 109936.
• Sungil Kim (2021), Maximum feasibility estimation, Information Sciences, 575, pp 739-801.
• Sungil Kim (2019), Revealing household characteristics using connected home products, Information Sciences, 486, pp 52-61.
• Sungil Kim and Heeyoung Kim (2016), A new metric of absolute percentage error for intermittent demand forecasts, International Journal of Forecasting, 32(3), pp 669-679.
특허
• Kim, Sungil (primary inventor), Method of anomaly detection of vessels applying Bayesian bootstrap. (10-2534357, granted May 16, 2023)
• Kim, Sungil (primary inventor), Sensor drift compensation method and device. (10-2364019, granted February 14, 2022)
• Kim, Sungil (primary inventor), Method and apparatus for determining delay possibility of shipment. (10-2250354, granted May 4, 2021)