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김아현 초빙교수 사진
김아현 초빙교수
Contact Information
  • Office.S246
  • Tel.02-958-3189
  • E-mail.ahyun1003@kaist.ac.kr
Research Areas Data Mining, Machine Learning
Biography

학력

    2010~2016 Ph.D in Applied Statistics, Yonsei University, Korea
    2007~2009 M.S. in Applied Statistics, Yonsei University, Korea
    2001~2007 B.S. in Applied Statistics, Yonsei University, Korea
    B.S. in Economics, Yonsei University, Korea

주요경력

    - Invited Professor, School of Management Engineering, KAIST (2020.9 ~ present )
    - Lecturer, School of Management Engineering, KAIST (2018.9 ~ 2020.8 )
    - Invited Professor, Department of Applied Statistics, Dankook University (2017.3 ~ 2020.2)
    - Lecturer, Department of Applied Statistics, Yonsei University (2010.9 ~ 2020.8)

산업체자문활동

    Teaching experience on topics such as introductory statistics, R and Python Programming, data science and machine learning, etc.
    - Samsung Electronics (2019)
    - SK Hynix (2019)
    - LG Display (2018)
    - Samsung SDS (2019, 2018, 2017, 2016)
Publications & Research

주요논문 (특허등)

    Kim, A. and Kim, H. (2020). "A New Classification Tree Method with Interaction Detection Capability," Submitted.
    Kim, A., Myung, J. and Kim, H. (2020). "Random Forest Ensemble Using a Weight-Adjusted Voting Algorithm," Journal of the Korean Data and Information Science Society, 31(2), 427-438.
    Kim, A., Kim, M. and Kim, H. (2014). "Double-Bagging Ensemble Using WAVE," Communications for Statistical Applications and Methods, 21, 411-422.
    Kim, A., Kim, J. and Kim, H. (2012). "The guideline for choosing the right-size of tree for boosting algorithm," Journal of the Korean Data and Information Science Society, 23, 949-959.
    Kim, A., Ha, M. and Kim, B.S. (2012). "Determining a BMDL of Blood Lead Based on ADHD Scores Using a Semi-Parametric Regression," The Korean Journal of Applied Statistics, 25, 389-401.
    Kim, D., Kim, A. and Kim, H. (2011). "Modified Recursive PCA," The Korean Journal of Applied Statistics, 24, 963-977.

연구분야

    - Data Mining
    - Machine Learning (Classification problems, Decision Tree & Ensemble models)
    - Deep Learning
    - Multivariate Data Analysis (Dimension Reduction, Data Visualization)
Teaching

경영통계분석(MGT503)

    MGT503Q Management Statical Analysis.pdf

    전산금융(FE539)

      FE539 Computational Finance1.pdf

      금융공학 인공지능 및 기계학습(FE540)

        FE540 AIMachineLearningForFinance.pdf
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