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Home PeopleFacultyEmeritus Faculty

Emeritus Faculty

프린트페이스북

Jeongyoun Ahn

Ph.D. in Statistics, UNC-Chapel Hill, 2006

  • High-dimensional Statistics Lab.
  • 042-350-3140 jyahn@kaist.ac.kr E2-2, #2104

Education

  • Seoul National University, B.S. & M.S. in Statistics
  • University of North Carolina at Chapel Hill, Ph.D. in Statistics

Research experience

  • 2006 ~ 2012 : Assistant Professor, University of Georgia
  • 2012 ~ 2021 : Associate Professor, University of Georgia
  • 2021 ~ Present : Associate Professor, KAIST

Research area

Research Interests:
 
• High Dimension, Low Sample Size Data Analysis
• Compositional Data Analysis for Gut Microbiome Studies 
• Regularized Multivariate Methods
• Dimension Reduction and Variable Selection
• Functional Data Analysis
• Interpretable Ordinal Multivariate Analysis
• Statistical Applications to Biological and Medical Problems 
• Outlier Detection 

Selected publications

  • Poythress, J. C.∗ , Ahn, J., and Park, C. (2021), A low-rank, orthogonally decomposable tensor regression with application to visual stimulus decoding with fMRI Data, Journal of Computational and Graphical Statistics, tentatively accepted.
  • Ma, Z.∗ and Ahn, J. (2021) Feature-weighted ordinal classification for predicting drug response in multiple Myeloma, Bioinformatics, accepted.
  • Park, J., Ahn, J., and Jeon, Y. (2021), Sparse functional linear discriminant analysis, Biometrika, asaa107
  • Chung, H. C.∗ and Ahn, J. (2021), Subspace rotations for high-dimensional outlier detection, Journal of Multivariate Analysis, 183, 104713.
  • Ahn, J., Chung, H. C.∗ , and Jeon, Y. (2020), Trace regularization for high-dimensional multi-class discrimination, Journal of Computational and Graphical Statistics, 30(1), 192–203.
  • Qiu, D.∗ and Ahn, J. (2020), Grouped variable screening for ultrahigh dimensional data under linear model, Computational Statistics and Data Analysis, 144, 106894.
  • Ahn, J., Lee, M. H., and Lee, J.∗ (2019), Distance-based outlier detection for high dimension, low sample size data, Journal of Applied Statistics, 46(1), 13–29.
  • Jung, S., Ahn, J. and Jeon, Y. (2019) Penalized orthogonal iteration for sparse estimation of generalized eigenvalue problem, Journal of Computational and Graphical Statistics, 28(3), 710–721
  • Jung, S., Lee, M. H., and Ahn, J. (2018), On the number of principal components in high dimensions, Biometrika, 105(2), 389–402.
  • Safo, S.∗ , Ahn, J., Jeon, Y. and Jung, S. (2018), Sparse Generalized Eigenvalue Problem for Canonical Correlation Analysis With Application to Integrative Analysis of Methylation and Gene Expression Data, Biometrics, 74(4), 1362–1371.
  • Park, J. and Ahn, J. (2017), Clustering Multivariate Functional Data with Phase Variation, Biometrics, 73(1): 324–333.
  • Kwon, S., Ahn, J., Jang, W., Lee, S., and Kim, Y. (2017), A Doubly Sparse Penalty Approach for Group Variable Selection, Annals of the Institute of Statistical Mathematics, 69:997–1025.
  • Safo, S.∗ and Ahn, J. (2016), General Sparse Multi-class Linear Discriminant Analysis, Computational Statistics and Data Analysis, 99:81–90.
  • Ahn, J. and Jeon, Y. (2015), Sparse HDLSS Discrimination with Constrained Data Piling, Computational Statistics and Data Analysis, 90:74–83.
  • Jeon, Y., Ahn, J., and Park, C. (2015), A Nonparametric Kernel Approach to Interval-Valued Data Analysis, Technometrics, 57 (4), 566-575.
  • Lee, J.∗ , Dobbin, K. K., and Ahn, J. (2014), Covariance Adjustment for Batch Effect in Gene Expression Data, Statistics in Medicine, 33(15):2681–2695.
  • Lee, M. H., Ahn, J. and Jeon, Y. (2013), HDLSS Discrimination with Adaptive Data Piling, Journal of Computational and Graphical Statistics, 22:433-451.
  • Ahn, J., Peng, M., Park, C., Jeon, Y. (2012), A Resampling Approach for Interval-Valued Data Regression, Statistical Analysis and Data Mining, 5:336–348.
  • Ahn, J., Lee, M. H., and Yoon, Y. J. (2012), Clustering High Dimension, Low Sample Size Data Using the Maximal Data Piling Distance, Statistica Sinica, 22(2):443–464.
  • Park, C., Ahn, J., Hendry, M.and Jang, W. (2011), Analysis of long period variable stars with nonparametric tests for trend detection, Journal of the American Statistical Association, 106(495):832–845.
  • Park, E., Spiegelman, C. and Ahn, J. (2011), A Nonparametric Approach Based on a Markov like Property for Classification, Chemometrics and Intelligent Laboratory Systems, 108:87–92.
  • Ahn, J. and Marron, J. S. (2010), The Maximal Data Piling Direction for Discrimination, Biometrika, 97:254–259.
  • Ahn, J. (2010), A Stable Hyperparameter Selection for the Gaussian RBF Kernel for Discrimination, Statistical Analysis and Data Mining, 3:142–148.
  • Park, C., Lazar, N., Ahn, J., and Sornborger, A. (2010), A Multiscale Analysis of the Temporal and Spatial Characteristics of Resting fMRI Data, Journal of Neuroscience Methods, 193:334–342.
  • Liu, Y., Zhang, H. H., Park, C., and Ahn, J. (2007), Support Vector Machines with Adaptive Lq Penalty, Computational Statistics and Data Analysis, 51:6380–6394.
  • Ahn, J., Marron, J. S., Muller, K.E. and Chi, Y. -Y. (2007), The High Dimension, Low Sample Size Geometric Representation Holds Under Mild Conditions, Biometrika, 94:760–766.
  • Marron, J. S., Todd, M. J., and Ahn, J. (2007), Distance Weighted Discrimination, Journal of the American Statistical Association, 102:1267–1271.
  • Zhang, H., Ahn, J., Lin, X., and Park, C. (2006), Gene Selection Using Support Vector Machines with Nonconvex Penalty, Bioinformatics, 22:88–95.

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