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교수진 검색
김영훈 교수
  • 최종학위
  • 고려대학교 공학박사
  • 전공분야
  • 인공지능, 기계학습 및 경영과학응용
  • 연구분야
  • Deep Learning, Machine Learning Algorithm, Manufacturing Process Control, Health Data Analytics
  • 연구실위치
  • 우정원 7086호
  • 연구실전화
  • 031-201-3661
  • 이메일
  • y.kim@khu.ac.kr
Ph.D., Korea University, School of Industrial Management Engineering, Korea, 2019. B.S., Korea University, School of Industrial Management Engineering, Korea, 2013.
주요경력 및 활동
[Work Experience]  Assistant Professor, Department of Industrial and Management Systems Engineering, Kyung Hee University, 2022 – Present.  Assistant Professor, School of Mathematics, Statistics, and Data Science, Sungshin Women’s University, 2020 – 2022  Adjunct Professor, Department of Semiconductor Engineering, Samsung Institute of Technology, 2020 – 2021  Data Scientist, Optimization & Analytics Office, SK innovation, 2019 – 2020 [Research Grants]  Rehabilitation System Development for Disabled People with Artificial Intelligence, National Rehabilitation Center (NRC), 2021 – 2023  Machine Learning Lecture for Students of Liberal Arts and Social Science, K-MOOC, Ministry of Education, 2021 – 2021  Electrical Fire Prevention System with IoT and Artificial Intelligence, Ministry of Land, Infrastructure and Transport (MOLIT), 2021 – 2022  Recycling Malfunctioned Gas Insulated Substation Controller with Data Analytics, Korea Institute for Advancement of Technology (KIAT), 2020 – 2022  Robust Deep Learning with Test-time Data Augmentation for Autonomous Vision Inspection, National Research Foundation (NRF), 2020 – 2021 [Industrial Projects]  Autonomous Logistics Systems in Semiconductor Manufacturing with Reinforcement Learning, Samsung Electronics (May 2020 – Dec 2020)  Chemical Production Control with Deep Neural Network and Optimization, SK Nexlene (Jun 2020 – Dec 2020)  Enhancing the Performance of Soft Sensor for Chemical Production System, SK innovation (Jul 2019 – Feb 2020)  Demand Forecasting and Logistics Optimization for Domestic Oil Products, SK innovation (Mar 2019 – Feb 2020)  Oil Price Analysis Based on News Articles with Deep Learning, SK innovation (Mar 2019 – Feb 2020)
 Kim, Y., Lee, M., Kim, S.B., (2021) “Swarm Ascending: Swarm Intelligence-based Exemplar Group Detection for Robust Clustering,” Applied Soft Computing, Vol. 102, 107062.  Kim, I., Kim, Y., Kim, S. (2020), “Learning Loss for Test-time Augmentation,” Neural Information Processing Systems (NeurIPS).  Kim, Y., Lee, J., Ahn, G., Santos, I.C., Schug, K.A., Kim, S.B. (2020), “Convolutional Neural Network for Preprocessing-free Bacterial Spectra Identification,” Journal of Chemometrics, Vol. 34, e3304.  Kim, Y., Do, H., Kim, S.B. (2020), “Outer-Points Shaver: Robust Graph-Based Clustering via Node Cutting,” Pattern Recognition, Vol.97, 107001.  Lee, S., Kim, Y., Kang, H., Lee, S.-K., Chung, S., Cheong, T., Shin, K., Park, J., Kim, S.B. (2020), “Intelligent Traffic Control for Autonomous Vehicle Systems Based on Machine Learning,” Expert Systems with Applications, Vol. 144, 113074.  Kim, Y., Kim, S.B. (2018), “Collinear Groupwise Feature Selection via Discrete Fusion Group Regression,” Pattern Recognition, Vol.83, pp.1–13.  Park, C., Kim, Y., Park, Y., Kim, S.B. (2018), “Multitask Learning for Virtual Metrology in Semiconductor Manufacturing Systems,” Computers and Industrial Engineering, Vol.123, pp.209–219.  Kim, Y., Kim, S.B. (2018), “Optimal False Alarm Controlled Support Vector Data Description for Multivariate Process Monitoring,” Journal of Process Control, Vol.65, pp.1–14.  Santos, I. C., Smuts, J., Choi, W.-S., Kim, Y., Kim, S. B., Schug, K.A. (2018), “Analysis of Bacterial FAMEs Using Gas Chromatography – Vacuum Ultraviolet Spectroscopy for the Identification and Discrimination of Bacteria,” Talanta, Vol.182, pp.536–543.  Kim, Y., Schug, K.A., Kim, S.B. (2015), “An Ensemble Regularization Method for Feature Selection in Mass Spectral Fingerprints,” Chemometrics and Intelligent Laboratory Systems, Vol.146, pp.322–328.
 Explainable Artificial Intelligence (XAI)  Robust Deep Learning against Data Variations  Interface between Machine Learning and Discrete Optimization  Manufacturing Process Control with Machine Learning  Bio and Health Data Analytics