Prof.전수영
Prof.전수영
직위 또는 학위 : 부교수
Tel : 044-860-1552
E-mail : scheon@korea.ac.kr
연구분야 : Monte Carlo Methods, Stochastic Optimization, Bioinformatics,
Bayesian Computation, Panel Data Analysis, Change-point and Regime Switching Analysis.
학력사항
Korea University, Seoul, South Korea
B.S., Mathematics, February 1994Korea University, Seoul, South Korea
M.S., Statistics, February 2002
Master’s Thesis: “A Study of Generalized Maximum Entropy Estimator for the Panel Regression Model”
Advisor: Dr. Seuck Heun Song, ProfessorTexas A&M University, College Station, TX
Ph.D., Statistics, May 2007
Dissertation: "Protein folding and Phylogenetic tree reconstruction using Stochastic Approximation Monte Carlo"
Advisor: Dr. Faming Liang, Professor국제 학술 논문
2008
Cheon, S. and Liang, F. (2008.01), Phylogenetic Tree Construction Using Sequential Stochastic Approximation Monte Carlo. BioSystems(1.784), 91, 1, 94-107. (SCI)
Lee, J.K., Williams, P.D. and Cheon, S. (2008.03). Data Mining in Genomics, Clinics in Laboratory Medicine(1.971), 28, 1, 145-166. (SCI-E)
Chung, H.K., Pise-Masison, C.A., Radonovich, M.F., Brady, J., Lee, J.K., Cheon, S., Markham, P., Cristillo, A. and Pal, R. (2008.12). Cellular Gene Expression Profiles in Rhesus Macaques Challenged Mucosally with a Pathogenic R5 Tropic Simian Human Immunodeficiency Virus Isolate, Viral Immunology(1.966), 21, 4, 411-423. (SCI)
2009
Cheon, S., Song, S.H. and Jung, B.C. (2009.06). Tests for Independence in a Bivariate Negative Binomial Model, Journal of the Korean Statistical Society(0.465), 38, 2, 185-190. (SCI-E)
Cheon, S. and Liang, F. (2009.11). Bayesian Phylogeny Analysis via Stochastic Approximation Monte Carlo, Molecular Phylogenetics and Evolution(3.609), 53, 2, 394-403. (SCI)
Cheon, S., Williams, P.D., Havaleshko, D.M., Jeong, H., Cheng, F. Theodorescu, D. and Lee, J.K. (2009.11). Concordant gene expression signatures predict clinical outcomes of cancer patients undergoing systematic therapy, Cancer Research(7.856), 69, 21, 8302-8309. (SCI)
Havaleshko, D.M., Smith, S.C., Cho, H., Cheon, S., Owens, C.R., Lee, J.K., Petricoin, E.F. and Theodorescu, D. (2009.11). Comparison of Global versus EGFR pathway profiling for prediction of Lapatinib sensitivity in bladder cancer, Neoplasia(5.946), 11, 11, 1185-1193. (SCI)
Liang, F. and Cheon, S. (2009.12). Monte Carlo dynamically weighted importance sampling for spatial models with intractable normalizing constants, Journal of Physics: Conference Series, 197.
2010
Cheon, S. and Kim, J. (2010.02). Multiple Change-point Detection of Multivariate Mean Vectors with Bayesian Approach, Computational Statistics and Data Analysis(1.028), 54, 2, 406-415. (SCI-E)
Kim, J. and Cheon, S. (2010.06). Bayesian Multiple Change-point Estimation with Annealing Stochastic Approximation Monte Carlo, Computational Statistics(0.276), 25, 2, 215-239. (SCI-E)
Kim, J. and Cheon, S. (2010.07). Bayesian Multiple Change-point Estimation Using SAMC, Proceedings of the Tenth Islamic Countries Conference on Statistical Sciences (ICCS-X), Volume II, The Islamic Countries Society of Statistical Sciences, Lahore: Pakistan, 823-830.
Kim, J. and Cheon, S. (2010.09). A Bayesian regime-switching time series model, Journal of Time Series Analysis(0.761), 5, 31, 365-378. (SCI)
2011
Cheon, S. and Liang, F. (2011.09). Folding small proteins via Annealing Stochastic Approximation Monte Carlo, BioSystems(1.784), 105, 3, 243-249. (SCI)
2014
Jung, B.C., So, S. and Cheon, S. (2014.03), Exact inference in contingency tables via stochastic approximation Monte Carlo, Journal of the Korean Statistical Society(0.465), 43, 1, 31-45. (SCI-E)
Cheon, S. and Kim, J. (2014.07), A Bayesian structural change analysis via Stochastic Approximation Monte Carlo and Gibbs Sampler. Journal of Statistical Computation and Simulation(0.629) , 84, 7, 1444-1470. (SCI-E)
Cheon, S. and Liang, F. (2014.07), Stochastic Approximation Monte Carlo Importance Sampling for Approximating Exact Conditional Probabilities, Statistics and Computing(1.429), 24, 4, 505-520. (SCI-E)
Kim, J. and Cheon, S. (2014.10), Stochastic approximation Monte Carlo Gibbs sampling for structural change inference in a Bayesian heteroscedastic time series model. Journal of Applied Statistics(0.449), 41, 10, 2157-2177. (SCI-E)
2017
Lim, H-K., Lee, J. and Cheon, S. (2017.01), Stochastic approximation Monte Carlo EM for change point analysis, Journal of Statistical Computation and Simulation(0.749), 87, 1, 69-87. (SCI-E)
Lim, H-K., Narisettyand, N. N., Cheon, S. (2017.02). Robust multivariate mixture regression models with incomplete data, Journal of Statistical Computation and Simulation(0.749), 87, 2, 328-347. (SCI-E)
Lee, J and Cheon, S. (2017.03), Estimation for the multi-way error components model with ill-conditioned panel data, Journal of the Korean Statistical Society(0.504) , 46, 1, 28-44. (SCI-E)
2018
Jung, B.C., Cheon, S., Lim, H-K. (2018.02). Mixtures of regression models with incomplete and noisy data, Communications in Statistics - Simulation and Computation(0.397), 47, 2, 444-463. (SCI-E) 참여연구원
· Bigdata Analysis: Social Network, Machine Learning
· Monte Carlo Methods: Markov chain Monte Carlo
Stochastic Approximation Monte Carlo
Monte Carlo Dynamically Weighted Importance Sampling
Monte Carlo Metropolis-Hastings· Stochastic Optimization
· Bioinformatics
· Bayesian Computation
· Change-point and Regime Switching Analysis
· Panel Data Analysis
· Bayesian analysis
· Monte Carlo Methods
· Machine Learning
· Statistical Learning
· 남소희 E-mail : sohee2702@korea.ac.kr, HP: 010-6624-****
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· 엄지은 E-mail : vip_eomg@naver.com, HP: 010-8468-****