Yang Shi, PhD

Assistant Professor

PhD (Biostatistics), University of Michigan, Ann Arbor, MI, 2016

(706) 721-5712
room number AE-1035
yang shi


Monte Carlo Methods; Resampling Methods; Multilevel Models; Numerical Methods; Genomics; Bioinformatics

 Dr. Yang Shi joined the Medical College of Georgia at Augusta University as an Assistant Professor of Biostatistics and Data Science in the Department of Population Health Sciences in 2018. He received his Ph.D. and M.S. degrees in Biostatistics from University of Michigan, Ann Arbor and his B.S. degree in Biological Science from Peking University. The primary research interest of Dr. Shi is developing statistical methodologies and computational techniques for the mining and analysis of large-scale genomic data. He is currently focusing on the improvement of the computational efficiency of resampling-based testing methods in genomic data analysis and the application of multilevel models for the study of complex genomic data. In addition, Dr. Shi has a broad range of interests in applying statistical methodologies and bioinformatics tools for solving scientific problems in biomedicine and public health. Specifically, he is interested in neuroscience and cancer research, and he collaborates with investigators on statistical modeling and data analysis in studying the genetic and molecular mechanism, population-based prevention or clinical treatment of different types of neurological and neuropsychiatric diseases and cancers.



Introduction to Statistical and Machine Learning; Intermediate Theoretical Statistics; High-throughput Genomic Data Analysis.



  • Shi Y, Wang M, Shi W, Lee J, Kang H, Jiang H. (2019). Accurate and efficient estimation of small p-values with the cross-entropy method: applications in genomic data analysis. Bioinformatics, 35 (14): 2441-2448.
  • Cust AE, Badcock C, Smith J, Thomas N, Haydu L, Armstrong B, Law M, Thompson J, Kanetsky P, Begg C, Shi Y, Kricker A, Orlow I, Sharma A, Yoo S, Leong S, Berwick M, Ollila DW, Lo S. (2019). British Journal of Dermatology, in press. Published online in advance in September 2019: https://doi.org/10.1111/bjd.18524.
  • Wang K, Shi Y, Li ZY, Xiao Y, Li J, Zhang X, Li H. (2019). Metastatic Pattern Discriminates Survival Benefit of Primary Surgery for De Novo Stage IV Breast Cancer: A Real-World Observational Study. European Journal of Surgical Oncology, 45(8):1364-1372.
  • Shi Y and Lee J. (2018). Sample size calculations for group randomized trials with unequal group sizes through Monte Carlo simulations. Statistical Methods in Medical Research, 27 (9): 2569-2580
  • Ding D, Shi W, Shi Y. (2018). Numerical simulation of embryo transfer: how the viscosity of transferred medium affects the transport of embryos. Theoretical Biology and Medical Modelling, 15 (1): 20.
  • Orlow I, Shi Y, Kanetsky PA, Thomas NE, Luo Li, Corrales-Guerrero S, Cust AE, Sacchetto L, Zanetti R, Rosso S, Armstrong BK, Dwyer T, Venn A, Gallagher RP, Gruber SB, Marrett LD, Anton-Culver H, Busam K, Begg CB, Berwick M and GEM Study Group (2018). The interaction between vitamin D receptor polymorphisms and sun exposure around time of diagnosis influences melanoma survival. Pigment Cell & Melanoma Research, 31 (2): 287-296.
  • Cuzick J, Myers O, Lee JH, Shi Y, Gage JC, Hunt WC, Robertson M, Wheeler CM and New Mexico HPV Pap Registry Steering Committee (2017). Outcomes in women with cytology showing atypical squamous cells of undetermined significance with vs without human papillomavirus testing. JAMA Oncology, 3 (10): 1327-1334.
  • Shi Y and Lee JH (2016). Sample size calculations for group randomized trials with unequal group sizes through Monte Carlo simulations. Statistical Methods in Medical Research, published online on December 15, 2016, https://doi.org/10.1177/0962280216682775
  • Shi Y, Chinnaiyan AM and Jiang H (2015). rSeqNP: A non-parametric approach for detecting differential expression and splicing from RNA-Seq data. Bioinformatics, 31 (13): 2222-2224.
  • Udager AM*, Shi Y*, Tomlins SA, Alva A, Siddiqui J, Cao X, Pienta KJ, Jiang H, Chinnaiyan AM and Mehra R (2014). Frequent discordance between ERG gene rearrangement and ERG protein expression in a rapid autopsy cohort of patients with lethal, metastatic, castration-resistant prostate cancer. The Prostate, 74 (12):1199-1208. *Contributed equally to this paper.
  • Shi Y and Jiang H (2013). rSeqDiff: Detecting Differential Isoform Expression from RNA-Seq Data Using Hierarchical Likelihood Ratio Test. PLOS ONE, 8 (11): e79448.