Yichen Cheng
Associate Professor Institute for Insight- Education
- Ph.D., statistics, Texas A&M University
- B.S., mathematics, Fudan University
- Specializations
- big and high dimensional data analysis
- text analytics
- health analytics
- application of novel learning methods in business
- Biography
Yichen Cheng is an Associate Professor in Business Analytics at the Robinson College of Business’s Institute for Insight. Her research interest is in developing efficient learning methods for big and high dimensional data with business applications in areas such as marketing, finance, health analytics, information systems, etc.
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, Journal of Management Information Systems, INFORMS Journal on Computing, Biometrika, Annals of Applied Statistics, etc. She currently serves as the Associate Editor of the Journal of Computational and Graphical Statistics.
At GSU, she has been teaching different courses for MSA (Master of Science in Analytics), MBA and PhD students, including regression analysis, predictive analytics, data visualization, statistical foundation for analytics, analytics experience, etc. She has offered multiple Bootcamps on SAS introduction, Linear Algebra, R introduction, and R visualization. She also leads sprints projects and works closely with companies from different industries to accommodate their needs through state-of-the-art machine learning techniques.
- Publications
- Baird, A., Cheng, Y., and Xia, Y. (2023) Determinants of outpatient substance use disorder treatment length-of-stay and completion: the case of a treatment program in the southeast US. Scientific Reports, 13 (1), 13961.
- Cheng, Y., Xia, Y., and Wang, X., (2023) Bayesian multitask learning for medicine recommendation based on online patient reviews. Bioinformatics, 39 (8), btad491.
- Jabr, W., Ghoshal, A., Cheng, Y., and Pavlou, P. (2023) A Clickstream-Based Approach to Predict Customer Behavior and Facilitate Conversion. Journal of Management Information Systems, 40 (2), 470-502.
- Wang, G., Cheng, Y, Xia, Y., Ling, Q., and Wang, X. (2023) Semi-supervised Bayesian keyphrase extraction. INFORMS Journal on Computing. 35 (3), 675-691.
- Baird, A. M., Cheng, Y., and Xia, Y. (2022) Use of Machine Learning to Examine Disparities in Completion of Substance Use Disorder Treatment. PLoS one, 17 (9): e0275054.
- 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.