Dr. Hyonho Chun
Adjunct Associate Professor, Department of Statistics, Purdue University, 2018-
Associate Professor, Department of Mathematics and Statistics, Boston University , 2018-
Associate Professor, Department of Statistics, Purdue University, 2016-2017
BrainPool Visiting Professor, Mathematical Sciences, KAIST, 2016
Assistant Professor, Statistics, Purdue University, 2010-2016
Postdoctoral Associate, Epidemiology and Public Health, Yale University, 2009- 2010
Ph. D. Statistics, University of Wisconsin at Madison, 2008
M. S. Statistics, Seoul National University, 2002
B. S. Earth Science Education, Seoul National University, 2000
Address:
We recently moved to Boston University.
Email: chunh@purdue.edu
or chunh@bu.edu
Research Interests:
Dimension reduction with count data.
Network inference and covariance estimation for high dimensional data.
Statistical genomics.
Computer model calibration and statistical quality control.
Statistical theory and methods for correlated high dimensional low sample size data.
Nonparametric function estimation and machine learning.
Research Assistants:
Past
Qingyi Gao (Ph. D. Student at Department of Statistics)
Tim Keaton (Ph. D. Student at Depratment of Statistics)
Ji Hwan Oh (Ph. D., Department of Statistics)
Seokhun Bang (M. S., Department of Industrial Engineering)
Andres Nocolas Lopez (B. S. )
Publications:
S. Jun, S. Lee and H. Chun (2019+), Learning dispatching rules using random forest in flexible job scheduling problems, International Journal of Production research, Accepted.
X. Feng, D. Guan, T. Auen, J. W. Choi, M. A. Salazar-Hernandez, J. Lee, H. Chun, F. Faruk, E. Kaplun, Z. Herbert, K.D. Topps and U. Ozcan (2019+), IL1R1 is required for celastrol's leptin sensitization and anti-obesity effects, Nature Medicine, Accepted.
H. Chun and H. Yang (2019+), A nonnegative robust linear model for deconvolution of proportions In Contemporary Biostatistics with Biopharmaceutical Applications, Accepted.
J. Cai, Q. Gao, H. Chun, H. Cai and T. Nantung (2019), Spatial autocorrelation in soil compaction and its impact on earthwork acceptance testing, Transportation Research Record, 0361198118822279.
J. Lee, A. J. Lee, J-K Lee, J. Park, Y. Kwon, S. Park, H. Chun, Y. Ju and D. Hong (2018), Mutalisk: a web-based somatic MUTation AnaLyIS toolKit for genomic, transcriptional and epigenomic signatures, Nucleic Acids Research, In Press.
H. Chun, M. H. Lee, S.-H. Kim and J. Oh (2018), Robust precision matrix estimation
via weighted median regression with regularization, Canadian Journal of Statistics, In Press.
J. Oh, F. Zhang, R. W. Doerge and H. Chun (2018), Kernel partial correlation: A novel approach to capturing conditional independence in graphical models for noisy data. Journal of Applied Statistics, In Press.
A.J. Kozik, C. H. Nakasu, H. Chun, Y.L. Jones-Hall (2017), Age, sex, and TNF associated differences in the gut microbiota of mice and their impact on acute TNBS colitis, Exp. Mol. Pathol., 103, 311-319.
D. Ma, Y. Kim, B. Cooper, J. Oh, H. Chun, J. Choe, J. P. Schoonmaker, K. M. Ajuwon, B. Min (2017), Metabolomics profiling to determine the effect of postmortem aging on color and lipid oxidative stabilities of different bovine muscles, Journal of Agricultural and Food Chemistry, 65, 6708-6716.
J. Shi, J. K. Knight, H. Chun, N. A. Guild, and J. M. Martil (2017), Using pre-assessment and in-class questions to change student understanding of molecular movements, Journal of Microbiology and Biology Education.
H. Chun, M. H. Lee, J. Fleet and J. Oh (2016), Graphical models via joint quantile regression with component selection, Journal of Multivariate Analysis, 152, 162-171.
H. Chun, X. Zhang and H. Zhao (2015), Gene regulation network inference with joint Gaussian graphical models, Journal of Computational and Graphical Statistics, 24(4), 954-974.
B. Li, H. Chun and H. Zhao (2014), On an additive semi-graphoid model for statistical networks with
application to pathway analysis, Journal of the American Statistical Association 109(507), 1188-1204.
H. Chun, M. Chen, B. Li and H. Zhao (2013), Joint conditional Gaussian graphical models with multiple sources of genomic data, Frontiers in Genetics: Bioinformatics and Computational Biology 4:294.
J. Moon and H. Chun (2013), Effective simultaneous confidence bands for repeated measurements in linear mixed-effect models, Journal of Statistical Computation and Simulation.
B. Li, H. Chun and H. Zhao (2012), Sparse estimation of conditional graphical models with application to gene networks, Journal of the American Statistical Association, 107:152-167.
H. Chun, D. Ballard, J. Cho and H. Zhao (2011), Association study
with multiple markers and disease via sparse partial least squares, Genetic Epidemiology, 35:479-486.
H. Chun, J. Kang, X. Zhang, M. Deng, H. Ma and H. Zhao (2011), Reverse
engineering of gene regulation networks with an application to the
DREAM4 in silico Network Challenge, Statistical Bioinformatics.
S. H. Cho and H. Chun (2010), Visualizing abnormal climate
changes in central America from 1996-2000, Computational Statistics.
H. Chun and S. Keles (2010), Simultaneous dimension reduction and
variable selection with Sparse Partial Least Squares, Journal of Royal
Statistical Society - Series B (Statistical Methodology), 72:3-25.
H. Shim, H. Chun, C. D. Engelman and B. A. Payseur (2009), Genome-wide
association studies using SNPs vs. haplotypes: An empirical comparison
with data from the North American Rhematoid Arthritis Consortium, BMC
Proceedings, 3 (Suppl 7):S35.
H. Chun and S. Keles (2009), Expression quantitative loci mapping with sparse partial least squares, Genetics, 182:79-90.
S. Keles and H. Chun (2008), Comments on: Augmenting Bootstrap to
analyze high dimensional genomic data. Connections to the ridge
regularized covariance estimator with bagging, TEST, 17(1): 36-39.
P.F. Kuan, H. Chun and S. Keles (2008), CMARRT: A tool for the
analysis of ChIP-chip data from tiling arrays by incorporating the
correlation structure. Pacific Symposium on Biocomputing, 13:515-526.