- Ph.D., statistics, Texas A&M University
- B.S., mathematics, Fudan University
- Big data analysis
- High dimensional data analysis
- Text analytics
- Health analytics
- Statistical genetics
Yichen Cheng is an assistant professor in Business Analytics at the Robinson College of Business’s Institute for Insight. Her research interest is in develop efficient learning methods for big data and high dimensional data, such as genomic data, text data and large scale spatial data.
Dr. Cheng obtained her Ph.D. in statistics from Texas A&M University. Before moving to Atlanta, she worked at the Fred Hutchinson Cancer research center as a post-doctoral research fellow where her research was on high dimensional data analysis with application in statistical genetics and cancer genomics.
Dr. Cheng's research has been published in premier Statistics and Business journals, including Journal of the American Statistical Association, Biometrika, INFORMS Journal on Computing, Annals of Applied Statistics and Biometrics. She currently serves as the Associate Editor of the Journal of Computational and Graphical Statistics.
At GSU, she has been teaching predictive analytics, data visualization for MSDA students and Analytics Experience for MBA students at the Robinson College of Business. She has offered multiple Bootcamps on SAS introduction, Linear Algebra and R visualization. She also leads sprints projects and work closely with companies from different industries to accommodate their needs through state-of-the-art machine learning techniques.
For more detailed information, please visit her Google Scholar.
- Cheng, Y., Wang, W., and Xia, Y. (2021) Supervised t-distributed stochastic neighbor embedding for data visualization and classification. INFORMS Journal on Computing, 33 (2), 566-585
- Wang, G., Cheng, Y., Chen, M., and Wang, W. (2021)Jackknife empirical likelihood confidence intervals for assessing heterogeneity in meta-analysis of rare binary event data. Contemporary Clinical Trials, 107, 106440
- Cheng, Y., and Zhao ,Y. (2109) Bayesian jackknife empirical likelihood. Biometrika 106 (4), 981-988
- Kundu, S., Cheng, Y., Shin, M., et al. (2018) Bayesian variable selection with graphical structure learning: Applications in integrative genomics. PloS one 13 (7), e0195070
- Cheng, Y., Dai, J.Y., Wang, X., Kooperberg, C. (2018) Identifying disease-associated copy number variations by a doubly penalized regression model. Biometrics 74 (4), 1341-1350
- Cheng, Y., Dai, J.Y., Paulson, T.G., Wang, X., Li, X., Reid, B.J., and Kooperberg, C., (2017) Quantification of multiple tumor clones using gene array and sequencing data (2017) Annals of Applied Statistics 11 (2), 967-991
- Cheng, Y., Dai, J.Y. and Kooperberg, C. (2016). Group Association Test Using a Hidden Markov Model. Biostatistics. 17 (2): 221-234.
- Cheng, Y. and Liang, F. (2015). Discussion on “Modeling an Augmented Lagrangian for Improved Blackbox Constrained Optimization” by Gramacy et al. Technometrics. In press.
- Wang, X., Li, X., Cheng, Y., Sun, X., Sun, X., Self, S., Kooperberg, C. and Dai, J.Y. (2015). Copy number alterations detected by whole-exome and whole-genome sequencing of esophageal adenocarcinoma. Human Genetics, 9:22.
- Liang, F., Cheng, Y., and Lin, G. (2014). Simulated Stochastic Approximation Annealing for Global Optimization with a Square-Root Cooling Schedule. Journal of the American Statistical Association, 109, 847–863.
- Cheng, Y., Gao, X, and Liang, F. (2014). Bayesian Peak Picking for NMR Spectra. Genomics, Proteomics & Bioinformatics, 12, 39–47.
- Liang, F., Cheng, Y., Song, Q., Park, J., and Yang, P. (2013). A Resampling-based Stochastic Approximation Method for Analysis of Large Geostatistical Data. Journal of the American Statistical Association, 108, 325–339.
- Zhao, H., Cheng, Y. and Bang, H. (2011). Some Insight on Censored Cost Estimators. Statistics in Medicine, 30, 2381–2389.
- Smith, M.L., Hochhalter, A.K., Cheng, Y., Wang, S., and Ory, M.G. (2011). Programmatic Inﬂuences on Outcomes of an Evidence-based Falls Program for Older Adults: A translational assessment. Translational Behavioral Medicine: Practice, Policy and Research, 1(3), 384–393.