Young-hoon Kim Professor
- Korea University Doctor of Engineering
- 인공지능, 기계학습 및 경영과학응용
- Deep Learning, Machine Learning Algorithm, Manufacturing Process Control, Health Data Analytics
Ph.D., Korea University, School of Industrial Management Engineering, Korea, 2019. B.S., Korea University, School of Industrial Management Engineering, Korea, 2013.
[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