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Sungil Kim

Associate Professor

Department of Industrial Engineering, UNIST

Biography

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.

The Principal Investigator, Sungil Kim, Ph.D., is an associate professor in the Department of Industrial Engineering. His research interests include industrial statistics and data analytics, quality engineering and management, and machine learning and data mining. He has served in a number of leadership positions at UNIST, domestically, and internationally. For more details, you can find his vitae.

Interests

  • Industrial Statistics
  • Quality Engineering and Management
  • Machine learning and Data mining

Education

  • PhD in Industrial Engineering, 2011

    Georgia Institute of Technology

  • MS in Statistics, 2007

    Georgia Institute of Technology

  • MS in Industrial Engineering, 2007

    Georgia Institute of Technology

  • BSc in Industrial Engineering, 2005

    Yonsei University

Research Areas

Data Analytics Lab pursues leading-edge research in the following areas:

Artificial Intelligence in Quality Engineering

System Monitoring & Anomaly Detection

Sequential Learning, Large-scale Calibration, and Uncertainty Quantification

Publications

List of all publications by the Data Analytics Lab

Quickly discover relevant content by filtering publications.
YongKyung Oh, Chiehyeon Lim, Junghye Lee, Sewon Kim, Sungil Kim (2022). Multichannel Convolution Neural Network for Gas Mixture Classification. Annals of Operations Research.

JuYeong Lee, JiIn Kwak, YongKyung Oh, Sungil Kim (2022). Quantifying Incident Impacts and Identifying Influential Factors on Urban Traffic Networks. Transportmetrica B: Transport Dynamics.

Sungil Kim (2021). Maximum feasibility estimation. Information Sciences.

YongKyung Oh, Sungil Kim (2021). Multi-channel Convolution Neural Network for Gas Mixture Classification. 2021 International Conference on Data Mining Workshops (ICDMW).

YongKyung Oh, Hanjoo Kim, Deokjung Lee, Sungil Kim (2021). Simulation-based Anomaly Detection in Nuclear Reactors. Journal of the Korean Institute of Industrial Engineers.

See all publications

Projects

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All Statistics Deep learning Optimization Quality Engineering Other

Household Profiling

Maritime Anomaly Detection

Mixture Gases Classification

Stowage Plan for Container Ships

Traffic Congestion Propagation

Meet the Team

Professors

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Sungil Kim

Associate Professor

Industrial Statistics, Quality Engineering and Management, Machine learning and Data mining

Researchers

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YongKyung Oh

PhD Student

Business Intelligence using machine learning, Data analytics based on statistical modeling, Data Augmentation using generative model

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Heesun Kim

MS Student

Data Mining and Statistical Analysis, Business Intelligence and Strategy based on data, Data driven R&D, Deep Learning, Machine Learning

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Jitae Yoo

Combined Master-Doctor

Industrial Stastics, Machine learning and Deep learning, Data Mining for Quality Control

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JongHwan Moon

MS Student

Statistical Data Mining, Dimensionality Reduction, Feature selection and Feature extraction

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SeungSu Kam

Combined Master-Doctor

Industrial Statistics, Quality Engineering and Management, Machine learning and Deep learning, Anomaly Detection using Unsupervised learning

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Giheon Koh

MS Student

Statistical computing and Data analysis, Data quality, Supervised learning

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Gyeongjun Kim

MS Student

Industrial Statistics, Machine learning and Data mining

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Byungkook Koo

MS Student

Data analytics, Machine learning and Data mining, Anomaly detection

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Ji-In Kwak

MS Student

Deep Learning, Data Analytics and Data mining, Optimization, Transportation

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Kwonin Yoon

MS Student

Optimization, Deep Learning, Machine Learning, Data analytics and Data mining, Logistics and Transportation

Alumni

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Juhui Lee

MS Student

Supervised learning, Data quality, Machine learning and Data mining

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Juyeong Lee

MS Student

Industrial Statistics, Machine learning and Deep Learning, Reinforcement learning, Traffic congestion propagation

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Namu Kim

Interns

Data mining, Smart home data analysis, Unsupervised learning

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Jaemin Park

MS Student

Deep Learning and Data Mining, Maritime Logistics, Quality Engineering and Management

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