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Table of contents

General Information

Full Name Jongmin Mun
Languages English, Korean, Japanese

Education

Work

  • 2020 - 2021
    Artificial Intelligence Researcher
    Center for Army Analysis and Simulations, Republic of Korea Army
    • Built data integration and oversampling pipeline using R and SQL to augment Army training data, mitigate class imbalance, and train SVM classifier enabling internal wildfire risk modeling in place of external indices
      • Resolved meteorological data deficiencies by integrating Army training data with Korea Meteorological Administration (KMA) databases via temporal and geospatial variables using SQL queries
      • Addressed severe class imbalance using generative model–based synthetic oversampling of wildfire cases
    • Pipeline development and data analysis results published in Journal of Classification as co–first author, collaborating with Army officers; contributed to pipeline implementation and real-data analysis
    • Enhanced pipeline with cost-sensitive training and derived theoretical guarantee, leading to first-author publication in "Computational Statistics & Data Analysis"; contributed to theory development, experiments, and writing

Honors and Awards

Services

  • 2025.10-2026.01
    Peer Reviewer
    Twenty-Ninth Annual Conference on Artificial Intelligence and Statistics (AISTATS)
  • 2025.05–2025.08
    Research Mentor
    USC JumpStart Program
    • Mentored Hein Win (undergraduate student at Calstate Polytechnique Pomona) on time series analysis

Teaching Experience

  • 2022.03-2022.12
    Graduate Teaching Assistant
    Yonsei University , Seoul, South Korea
    • STA3109 Mathematical Statistics II (Spring 2022, Fall 2022)
    • STA3102 Multivariate Analysis (Fall 2022)

Publications

  • Mun J., Bang, S., and Kim, J. (2025). Weighted support vector machine for extremely imbalanced data. Computational Statistics & Data Analysis, 203, 108078.
  • Namgung, J.Y.*, Mun, J.*, Park, Y., Kim, J., and Park, B.-Y (2024). Sex differences in autism spectrum disorder using class imbalance adjusted functional connectivity, NeuroImage, 304, 120956.
  • Nam, J.*, Mun, J.*, Jo, S., and Kim, J. (2024). Prediction of forest fire risk for artillery military training using weighted support vector machine for imbalanced data. Journal of Classification, 41, 170-189.
  • Park, Y., Kwon, Y., Lee, D., Kim, S., Mun, J., Kim, J., Jung, H., Cheon, J., Chang, J., and Park, J. (2024). In-vivo integration of soft neural probes through high-resolution printing of liquid electronics on the cranium. Nature Communications, 15, 1772.
    • Preliminary version presented at the 2024 International Conference of the IEEE Engineering in Medicine and Biology Society

Preprints

  • Mun, J., Kwak, S., and Kim, I. (2024). Minimax optimal two-sample testing under local differential privacy. arXiv preprint arXiv:2411.09064.
  • Mun, J., Dubey, P., and Fan, Y. (2025). High-dimensional sparse clustering via iterative semidefinite programming relaxed K-means. arXiv preprint arXiv:2505.20478.
  • Mun, J., Xu, X., and Fan, Y. (2025). Offline dynamic pricing under covariate shift and local differential privacy via twofold pessimism.
    • Accepted at Advances in Neural Information Processing Systems (NeurIPS) 2025 MLxOR Workshop