최규빈
- 전공
- Geometric Deep Learning
- 직위(직급)
- 부교수
- 연락처
- (063)270-3387
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- 자연대본관 205호
연구업적
Grants
• Geometric Deep Learning: Statistical Methodology for Non-Euclidean Data, 생애 첫 연구 (2021.09–2022.08)
• A Study of Multiscale Methods in Non-Euclidean Data, 지역대학우수과학자 (2023.06–2029.05)
Full Paper List
For a complete list of publications, visit Google Scholar.
Packages
• EPT — Ensemble Patch Transformation (Language: R, Repository: CRAN)
• Geometric Deep Learning: Statistical Methodology for Non-Euclidean Data, 생애 첫 연구 (2021.09–2022.08)
• A Study of Multiscale Methods in Non-Euclidean Data, 지역대학우수과학자 (2023.06–2029.05)
Full Paper List
For a complete list of publications, visit Google Scholar.
Packages
• EPT — Ensemble Patch Transformation (Language: R, Repository: CRAN)
Representative Publications
• Choi, G., & Oh, H. S. (2025). Exploring multiscale methods: reviews and insights. Journal of the Korean Statistical Society, 1–38.
• Choi, S., & Choi, G. (2025). Gode: graph Fourier transform based outlier detection using empirical Bayesian thresholding. Journal of the Korean Statistical Society, 54(2), 496–516.
• Choi, G., & Oh, H. S. (2024). Decomposition via elastic-band transform. Pattern Recognition Letters, 182, 76–82.
• Choi, G., & Oh, H. S. (2023). Elastic-band transform for visualization and detection. Pattern Recognition Letters, 166, 119–125.
• Kim, D., Choi, G., & Oh, H. S. (2020). Ensemble patch transformation: a flexible framework for decomposition and filtering of signal. EURASIP Journal on Advances in Signal Processing, 2020(1), 1–27.
• Choi, G., Oh, H. S., & Kim, D. (2018). Enhancement of variational mode decomposition with missing values. Signal Processing, 142, 75–86.
• Choi, S., & Choi, G. (2025). Gode: graph Fourier transform based outlier detection using empirical Bayesian thresholding. Journal of the Korean Statistical Society, 54(2), 496–516.
• Choi, G., & Oh, H. S. (2024). Decomposition via elastic-band transform. Pattern Recognition Letters, 182, 76–82.
• Choi, G., & Oh, H. S. (2023). Elastic-band transform for visualization and detection. Pattern Recognition Letters, 166, 119–125.
• Kim, D., Choi, G., & Oh, H. S. (2020). Ensemble patch transformation: a flexible framework for decomposition and filtering of signal. EURASIP Journal on Advances in Signal Processing, 2020(1), 1–27.
• Choi, G., Oh, H. S., & Kim, D. (2018). Enhancement of variational mode decomposition with missing values. Signal Processing, 142, 75–86.
Books
Not applicable.
Workshop & Conference Papers
• Heavy-Snow Transform: A New Multiscale Method for Time Series (The 5th Institute of Mathematical Statistics Asia Pacific Rim Meeting, National University of Singapore, June 2018.)
• Elastic-Band Transform: A New Approach to Multiscale Visualization (Joint Meeting of the 10th Asian Regional Section (ARS) of the International Association for Statistical Computing (IASC) and the NZ Statistical Association (NZSA), The University of Auckland, December 2017.)
• Modified Variational Mode Decomposition using Ebayesthresh (The 4th Institute of Mathematical Statistics Asia Pacific Rim Meeting, The Chinese University of Hong Kong, June 2016.)
• Elastic-Band Transform: A New Approach to Multiscale Visualization (Joint Meeting of the 10th Asian Regional Section (ARS) of the International Association for Statistical Computing (IASC) and the NZ Statistical Association (NZSA), The University of Auckland, December 2017.)
• Modified Variational Mode Decomposition using Ebayesthresh (The 4th Institute of Mathematical Statistics Asia Pacific Rim Meeting, The Chinese University of Hong Kong, June 2016.)