Nak-Kyeong Kim

Assistant Professor
2307 E&CS
Mathematics and Statistics
Old Dominion University
Norfolk, VA 23529
(757) 683-3911
nxkim@odu.edu

Experiences

• Assistant Professor (2008 ~ Present)
• Department of Mathematics and Statistics, Old Dominion University, Norfolk, Virginia

• Postdoctoral Fellow (2005 ~ 2008)
• National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, Maryland
• Supervisor: John L. Spouge, M.D., Ph.D.

• Research Assistant (2004 ~ 2005)
• Department of Statistics, Purdue University, West Lafayette, Indiana                  

• Statistical Consultant (2001)
• Department of Statistics, Purdue University, West Lafayette, Indiana                                    

• Teaching Assistant (1999 ~ 2004)
• Department of Statistics, Purdue University, West Lafayette, Indiana           

Education

• Ph.D. in Statistics, 2005
• Purdue University, West Lafayette, Indiana
• Thesis: Bayesian Models and Markov Chain Monte Carlo Methods for Identifying Protein Motifs Using the Secondary Characteristics
• Advisor: Jun Xie, Ph.D.

• M.S. in Statistics, 2001
• Purdue University, West Lafayette, Indiana

• B.S. in Computer Science and Statistics, 1999
• Seoul National University, Seoul, South Korea

Teaching

• STAT 130M Elementary Statistics
• STAT 405/505 Introduction to Data Handling
• STAT 437/537 Applied Regression Analysis
• STAT 460/560 Statistical Simulation/Programming using Statistical Software Packages
• STAT 626 Mathematical Statistics II
• STAT 640 Survival Analysis

Research Interests

• Statistical genomics and bioinformatics
• Statistical modeling for ChIP-seq data
• Next-generation sequencing data analysis
  
• Bayesian modeling and Markov chain Monte Carlo methods
• Transcription factor binding sites identification in DNA sequence
• Protein motif identification

• AGLAM
• NEXT-peak
  

Publications

• Jun Xie and Nak-Kyeong Kim (2005) Bayesian Models and Markov Chain Monte Carlo Methods with the Secondary Characteristics, Journal of Computational Biology. Vol. 12 No. 7, pp 952-970

• Nak-Kyeong Kim, Kannan Tharakaraman, and John L. Spouge (2006) Adding sequence context to a Markov background model improves the identification of regulatory elements, Bioinformatics, Vol. 22 No. 23 pp 2870-2875

• Nak-Kyeong Kim and Jun Xie (2006) Protein Multiple Alignment Incorporating Primary and Secondary Structure Information, Journal of Computational Biology, Vol. 13 No. 9, pp 1615-1629

• Nak-Kyeong Kim, Kannan Tharakaraman, Leonardo Marino-Ramirez, and John L. Spouge (2008) Finding sequence motifs with Bayesian models incorporating positional information: an application to predicting transcription factor binding sites, BMC Bioinformatics, 9:262

• Loren Hansen, Nak-Kyeong Kim, Leonardo Mariño-Ramírez, and David Landsman (2011) Analysis of biological features associated with meiotic recombination hot and cold spots in Saccharomyces cerevisiae, PLoS ONE 6(12): e29711

• Sanghoon Son, Asad Khattak, and Nak-Kyeong Kim (2013) Non-coverage Errors in Travel Surveys Due to Mobile Phone Only Households, Journal of the Transportation Research Board, to appear

• Rasika Jayatillake and Nak-Kyeong Kim (2013) A Hierarchical Bayesian Model for the Mortality Rates of Virginia, submitted

• Nak-Kyeong Kim, Rasika Jayatillake, and John L. Spouge (2013) NEXT-peak: a normal-exponential two-peak model for peak-calling in ChIP-seq data, submitted

• Rasika Jayatillake, John L. Spouge, and Nak-Kyeong Kim (2013) Finding Multiple Binding Sites of a Transcription Factor within a Short Region of the Genome: a Bayesian Approach, to submit

Professional Services

• Reviewer of Bioinformatics
• Reviewer of Computational Statistics and Data Analysis
• Reviewer of Journal of Statistical Computation and Simulation  
• Reviewer of Statistical Applications in Genetics and Molecular Biology