Assistant Professor
Department
of Statistics
University
of Georgia
Phone:
(706) 542-3433, Office: 107 Statistics Building
Email: jy”last name” at uga ddot edu
Education
Ph.D.
in Statistics (2006), Department of Statistics & Operations Research,
University of North Carolina at Chapel Hill
MS, BS in
Statistics (1999), Department of Computer Science & Statistics, Seoul
National University
Research Interests
High
dimension, low sample size inference
Machine
learning; statistical learning theory
Statistical
problems in fMRI data
Publications
·
Lee, M. H., Ahn,
J. & Jeon, Y. (2011), HDLSS Discrimination with Adaptive Data Piling, submitted.
·
Park, E., Spiegelman,
C. & Ahn, J. (2011), A Nonparametric Approach Based on a Markov like Property for
Classification, Chemometrics
and Intelligent Laboratory Systems, tentatively accepted.
·
Park, C., Ahn,
J., Hendry, M. & Jang, W. (2011), Analysis
of Long Period Variable Stars using a Nonparametric
Significance Test of No Trend, JASA, accepted.
·
Ahn, J.,
Lee, M. H., & Yoon, Y. J. (2011), High Dimension, Low Sample Size
Clustering with the Maximal Data Piling Distance, Statistica
Sinica, accepted.
·
Review of Principles and Theory for Data Mining and Machine Learning
Principles and Theory for Data Mining and Machine Learning, by Clarke,
Fokoue, and, Zhang (2011), JASA, 106(493):
375-382
·
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
·
Ahn, J.
(2010), A Stable Hyperparameter
Selection for the Gaussian RBF Kernel for Discrimination, Statistical Analysis and Data
Mining, 3(3):142-148
·
Ahn, J. and Marron, J. S. (2010), The Maximal Data Piling Direction for Discrimination,
Biometrika, 97(1):254-259
·
Marron, J. S., Todd, M. J., and Ahn, J.
(2007), Distance Weighted Discrimination, JASA, 102(480):
1267- 1271
·
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(3):760-766.
·
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,
(extended version of the proceeding)
·
Liu, Y.,
Zhang, H. H., Park, C., and Ahn, J. (2007),
The Lq Support Vector Machines,
Proceedings of Joint Summer Research Conference on Machine and Statistical
Learning: Prediction and Discovery, Contemporary Mathematics, 443, 35-48.
·
Zhang, H., Ahn, J., Lin, X., and Park, C. (2006), Gene Selection Using
Support Vector Machines with Nonconvex Penalty,
Bioinformatics, 22, 88–95.
·
Robinson III,
W. P., Stiffler, A., Rutherford, E. J., Ahn, J., Hurd, H., Baker, C. C.,
Meyer, A., and Rich, P. B. (2004), Blood Transfusion is an Independent
Predictor of Increased Mortality in Nonoperatively Managed Blunt Hepatic and Splenic Injuries,
Journal of Trauma-Injury Infection & Critical Care,
58(3):437 - 445.
·
Ahn, J. and Park, S. H. (1999), Optimal Restrictions on
Regression Parameters For Linear Mixture
Model, Journal of Korean Statistical Society, Vol. 28, No. 3, 325 - 336.
Links