Research

Published and Accepted papers

1. Lim, J., Yu, D. and Pyun, K. S. (2011). Hyper-parameter in hidden Markov random field. The Korean Journal of Applied Statistics, 24(1), 177–183.

2. Yu, D., Lim, J., Liang, F., Kim, K. and Kim, B. S. (2012). Permutation test for incomplete paired data with application to cDNA microarray data. Computational Statistics and Data Analysis, 56(3), 510–521.

3. Lim, J. Kim, J., Kim, S., Yu, D., Kim, K., and Kim, B. S. (2012). Detection of differentially expressed gene sets in a partially paired microarray data set. Statistical Applications in Genetics and Molecular Biology, 11(3), Article 5.

4. Lim, J., Lee, K., Yu, D., Liu, H., and Sherman, M. (2012). Parameter estimation in the spatial auto-logistic model with working independent subblocks. Computational Statistics and Data Analysis, 56(12), 4421–4432.

5. Yu, D. and Lim, J. (2013). Introduction to general purpose GPU computing. Journal of the Korean Data & Information Science Society, 24(5), 1043–1061.

6. Yu, D., Kim, M., Xiao, G. and Hwang, T. H. (2013). Review of biological network data and its applications. Genomics & Informatics, 11(4), 200–210.

7. Lee, S. H., Yu, D., Bachman, A. H., Lim, J. and Ardekani, B. A. (2014). Application of fused lasso logistic regression to the study of corpus callosum thickness in early Alzheimer’s disease. Journal of Neuroscience Methods, 221, 78–84.

8. Won, J., Lim, J., Yu, D., Kim, B. S. and Kim, K. (2014). Monotone false discovery rate. Statistics & Probability Letters, 87, 86–93.

9. Ng, C. T., Lim, J., Lee, K. E., Yu, D. and Choi, S. (2014). A fast algorithm to sample the number of vertexes and the area of the random convex hull on the unit square. Computational Statistics, 29, 1187–1205. doi:10.1007/s00180-014-0486-1.

10. Yu, D., Lee, S. J., Lee, W. J., Kim, S., Lim, J. and Kwon, S. W. (2015). Classification of spectral data using fused lasso logistic regression. Chemometrics and Intelligent Laboratory Systems, 142, 70–77.

11. Yu, D., Won, J., Lee, T., Lim, J. and Yoon, S. (2015). High-dimensional fused lasso regression using majorization-minimization and parallel processing. Journal of Computational and Graphical Statistics, 24(1), 121–153.

12. Yu, D. and Kim, B. (2015). A study on tuning parameter selection for MDPDE. Journal of the Korean Data & Information Science Society, 26(3), 549–559.

13. Jang, W., Lim, J., Lazar, N. A., Loh, J. M. and Yu, D. (2015). Some Properties of Generalized Fused Lasso and Its Applications to High Dimensional Data. Journal of the Korean Statistical Society, 44(3), 352–365.

14. Yu, D., Son, W., Lim, J. and Xiao, G. (2015). Statistical completion of partially identified graph with application to estimation of gene regulatory network. Biostatistics, 16(4), 670–685.

15. Park, S., Kim, S., Yu, D., Pena-Llopis, S., Gao, J., Park, J. S., Chen, B., Norris, J., Wang, X., Chen, M., Kim, M., Yong, J., Wardak, Z.,  Choe, K., Story, M., Starr, T., Cheong, J.-H., and Hwang, T. H. (2016). An integrative somatic mutation analysis to identify pathways linked with survival outcomes across 19 cancer types. Bioinformatics, 32(11), 1643–1651.

16. Lee, S. H., Bachman, A. H., Yu, D., Lim, J., Ardekani, B. A., and for the Alzheimer’s Disease Neuroimaging Initiative. (2016). Predicting progression from mild cognitive impairment to Alzheimer’s disease using longitudinal callosal atrophy. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 2, 68–74.

17. Kim, B. and Yu, D. (2016). BCDR algorithm for network estimation based on pseudo-likelihood with parallelization using GPU. Journal of the Korean Data & Information Science Society, 27(2), 381–394.

18. Yu, D. (2016). A study on bias effect of LASSO regression for model selection criteria. The Korean Journal of Applied Statistics, 29(4), 643–656.

19. Park, D., Yu, D., Kim, T. Y. (2016). Toward understanding the evolution of pathogens: complex system-based hierarchical genomic inference (CS-HGI). Quantitative Bio-Science, 35(1), 1–6.

20. Son, W., Lim, J., and Yu, D.  (2016). Detection of Multiple Change Points Using Penalized Least Square Methods: A Comparative Study Between L0 and L1 Penalty. The Korean Journal of Applied Statistics, 29(6), 1147–1154.

21. Yu, D., Lim, J., Wang, X., Liang, F. and Xiao, G. (2017). Enhanced Construction of Gene Regulatory Networks using Hub Gene Information. BMC Bioinformatics, 18:186.

22. Kim, S. C. and Yu, D. (2017). Identification of differentially expressed genes using tests based on multiple imputations. Quantitative Bio-Science, 36(1), 23–31.

23. Kim, J., Yu, D., Lim, J., and Won, J. (2018). A peeling algorithm for multiple testing on a random field. To appear in Computational Statistics, 33(1), 503–525.

24. Lee, J. H., Park, J., and Yu, D. (2018). Identifying factors associated with university hospitals' profitability based on fused lasso regression. Journal of the Korean Data & Information Science Society, 29(1), 83–96.

25.  Kim, B., Yu, D., and Won, J. (2018). Comparative study of computational algorithms for the Lasso with high-dimensional, highly correlated data. Applied Intelligence, 48(8), 1933–1952.

26.  Park, H., Kang, J., Heo, S., and Yu, D. (2018). Comparative study of prediction models for corporate bond rating. The Korean Journal of Applied Statistics, 33(3), 367–382.

27. Yu, D., Lee, S. H., Lim, J., Xiao, G., Craddock, R. C., and Biswal, B. B. (2018). Fused Lasso Regression for Identifying Differential Correlations in Brain Connectome Graphs. Statistical Analysis and Data Mining, 11(5), 203–226.

28. Kim, S. C., Yu, D., and Cho, S. B. (2018). COEX-Seq: Convert a Variety of Measurements of Gene Expression in RNA-Seq, Genomics & Informatics,  16(4), e36.

29. Choi, Y.-G., Jeong, S., and Yu, D. (2018). A study on bias effect on model selection criteria in graphical lasso. Quantitative Bio-Science, 37(2), 133–141.

30. Zhang, M., Li, Q., Yu, D., Yao, B., Guo, W., Xie, Y., and Xiao, G. (2019). GeNeCK: a web server for gene network construction and visualization. BMC Bioinformatics, 20:12.

31. Choi, Y.-G. and Yu, D. (2019). Causal inference from nonrandomized data: key concepts and recent trends. The Korean Journal of Applied Statistics, 32(2), 173–185.

32. Lim, J., Yu, D., Kuo, H., Choi, H., and Walmsley, S. (2019). Truncated rank correlation (TRC) as a robust measure of test-retest reliability in mass spectrometry data. Statistical Applications in Genetics and Molecular Biology, 18(4).

33. Ko, S., Yu, D., and Won, J. (2019). Easily parallelizable and distributable class of algorithms for structured sparsity, with optimal acceleration. Journal of Computational and Graphical Statistics, 28(4), 821–833.

34. Park, H., Lee, S., Lee, E.-J., Cho, Y., Park, Y. S., Lee, J., and  Yu, D. (2020). Short-term forecasting for sea surface temperature based on tidal observatory observations. Journal of the Korean Data & Information Science Society, 31(2), 255–271. (Appendix A and B).

35. Lee, S., Cho, Y., Lee, J. S., and  Yu, D. (2020). Comparative study of recommender systems using movie rating data. Journal of the Korean Data & Information Science Society, 31(6), 975–991.

36. Cho, Y., Yu, D., Son, W., and Park, S.  (2020). Introduction to numba library in Python for efficient statistical computing. The Korean Journal of Applied Statistics, 33(6), 665–682.

37. Choi, H., Lim, J., Yu, D., and Kwak, M. (2021). Two-sample test for interval-valued data. Journal of the Korean Statistical Society, 50, 233–271.

38. Lee, G., Son, W., Lee, S., and Yu, D. (2021). An empirical comparison of the CUSUM and the FLSA for change points detection. Journal of the Korean Data & Information Science Society, 32(6), 1-12.

39. Lee, J. S., Cho, Y., Lee, J., Ko, G. W., and Yu, D. (2022). A study on bias effect of elastic net penalized regression for model selection. Journal of the Korean Data & Information Science Society, 33(1), 67-68.

40. Choi, Y.-G., Lee, S., and Yu, D. (2022). An efficient parallel block coordinate descent algorithm for large-scale precision matrix estimation using graphics processing units. Computational Statistics, 37(1), 419-443.

41. Cho, Y., Kim, J., and Yu, D. (2022). Comparative Study of CUDA GPU Implementations in Python With the Fast Iterative Shrinkage-Thresholding Algorithm for LASSO. IEEE ACCESS, 53324-53343.


42. Yu, D., Lim, J., and Son, W. (2022). An empirical evidence of inconsistency of the 𝓁₁ trend filtering in change point detection. The Korean Journal of Applied Statistics, 35(3), 371-384.


43. Jun, B., Jara-Figueroa, C., and Yu, D. (2022). The economic resilience of a city: the effect of relatedness on the survival of amenity shops during the COVID-19 pandemic, Cambridge Journal of Regions, Economy and Society, 15, 551-573.


44. Jung, H., Cho, Y., Ko, G., Song, J., and Yu, D. (2023). Comparison study of synthetic data generation methods for credit card transaction data. Journal of the Korean Data & Information Science Society, 34, 49-72.


45. Lee, S., Kim, S. C., and Yu, D. (2023+). An efficient GPU-parallel coordinate descent algorithm for sparse precision matrix estimation via scaled lasso. To appear in Computational Statistics.


46. Son, W., Lim, J., and Yu, D. (2023+). Path algorithms for fused lasso signal approximator with application to COVID-19 spread in Korea. To appear in International Statistical Review.


47. Cho, S., Yu, D., and Lim, J. (2023+). Random Walk on Restricted Permutation Graph and Testing Independence of Bivariate Incomplete Data. To appear in Journal of Korean Statistical Society.


Working Papers

• Son, W., Lim, J., and Yu, D. (2023+). Tuning parameter selection for the fused lasso signal approximator. 

• Son, W., Lim, J. and Yu, D. (2023+). Path algorithms for fused lasso signal approximator with application to COVID-19 spread in Korea.

• Won, J., Park, B., and Yu, D. (2023+). Block-sparse extension of pseudo likelihood approach in precision matrix estimation. In preparation.

• An S., Kim, E., Park, S. Y., Han, Y., Min, J., and Yu, D. (2023+). Human sample identification from decomposition odors using gas chromatography mass spectrometry. In preparation.

• Yu, D., Lim, J. and Xiao, G. (2023+). Reconstruction of Gene Regulatory Network by Joint Precision Matrix Estimation Based on Sparse Direct Association of Gene Expression and Copy Number Variation. In preparation.

• Cho, Y., Ryu, H., Han, Y., Kim, H., An, S., Min, J, and Yu, D. (2023+). Using foot odor for sex prediction by gas chromatography mass spectrometry. In preparation.



Presentations

1. “Estimation of Gaussian graphical model with partially known graph information.” 2nd International workshop of the ERCIM working group on computing & statistics at Limasol, Cyprus (Oct. 2009).

2. “Permutation test for incomplete paired data with application to cDNA microarray data.” IMS-APRM (Poster) at Seoul, Korea (Jun. 2009); Joint meeting of Japan-Korea special conference of statistics and the 2nd Japan-Korea statistics conference of young researchers at Okayama, Japan (Feb. 2010).

3. “Estimation of piecewise constant function from correlated signals in an fMRI experiment.” KSS-ICSP 2011 (Poster) at Busan, Korea (Jul. 2011).

4. “Estimation of the shape constrained partially linear model and the liquidity cost.” International conference on advances in probability and statistics at Hongkong, China (Dec. 2011).

5. “MM-GPU algorithm for fused lasso regression.”, 2012-Fall Korean statistical society meeting at Seoul, Korea (Nov. 2012).

6. “Statistical completion of partially identified graph with application of estimation to gene regulatory network.”, 2014 Conference of Texas statisticians (Poster) at UT Dallas, USA (Mar. 2014); 2015-Spring Korean statistical society meeting at Cheongju, Korea (May 2015).

7. “Construction of gene regulatory network incorporating potential hub gene information.”, 2014-Fall Korean Data & Information Science Society meeting at Daegu, Korea (Nov. 2014).

8. “Classification of spectral data using fused lasso logistic regression.”, 2015-Spring Korean Data & Information Science Society meeting at Busan, Korea (May 2015); 2015-Fall Korean statistical society meeting at Yongin, Korea (Nov. 2015).

9. “BCDR algorithm for network estimation based on pseudo-likelihood with parallelization using GPU.”, 2016-Spring Korean statistical society meeting at Daegu, Korea (May 2016).

10. “A study on bias effect of LASSO regression for model selection criteria.”, 2016-Spring Korean Data & Information Science Society meeting at Gyeongsan, Korea (May 2016).

11. “Enhanced Construction of Gene Regulatory Networks using Hub Gene Information.”, The 4th Institute of Mathematical Statistics Asia Pacific Rim Meeting at Hongkong, China (Jun. 2016); 2017-Spring Korean statistical society meeting at Seoul, Korea (May. 2017).

12. “Penalized regression method for finding differences in brain connectome graphs.”, 2016 Joint Statistical Meetings at Chicago, USA (Aug. 2016); EcoSta 2017 at Hong Kong (Jun. 2017).

13. “Sparse partial correlation estimation with scaled lasso.”, Young Statistician’s Meeting 2017 at Yangpyeong  (Jun., 2017).

14. “A Continuum of Optimal Primal-Dual Algorithms for Convex Composite Minimization Problems with Applications to Structured Sparsity.”, 2018 ICSA China Conference with the Focus on Data Science at Qingdao, China (Jul., 2018).

15. “Short-term Forecasting for Sea Surface Temperature based on Real-time observations from Tidal Observatory.”, 2018-Fall The Korean society of oceanography meeting at Busan, Korea (Oct. 2018).

16. “Human sample identification from decomposition odors using gas chromatography mass spectrometry.”, The 35th Korean society of forensic science meeting at Wonju, Korea (Nov., 2018).

17. “Fused lasso regression for identifying differential correlations in brain connectome graphs.”, CMStatistics 2018 at Pisa, Italy (Dec., 2018).

18. “Comparative study of prediction models for corporate bond rating”, 2017-Spring Korean statistical society meeting at Chuncheon, Korea (May. 2019).

19. “Efficient GPU-parallel coordinate descent algorithm for sparse precision matrix estimation via scaled Lasso.”, EcoSta 2019 at Taichung, Taiwan (Jun., 2019).

20. “Short-term forecasting for Korean coastal sea surface temperature and monitoring its levels based on Machine-Learning algorithms”, PICES-2019 Annual Meeting at Victoria, Canada. (Oct. 2019).

21. “An efficient parallel block coordinate descent algorithm for large-scale precision matrix estimation using GPUs”, EcoSta 2021, Virtual meeting (Jun. 2021).

22. “Sparse partial correlation estimation via scaled Lasso”, ISI World Statistics Congress 2021, Virtual meeting (Jul. 2021).

23. “An efficient parallel block coordinate descent algorithm for large-scale precision matrix estimation using GPUs”, IASC-ARS 2022 (Hybrid Conference), Kyoto, Japan. (Feb. 2022).


24. "Path algorithms for fused lasso signal approximator with application to COVID-19 spread in Korea", IASC-ARS Interim Conference 2022 (Dec. 2022).


25. "Comparison study of synthetic data generation methods for credit card transaction data", 2023 Korea's Allied Economic Associations Annual Meeting (Feb. 2023).


Grants