Shuo Chen, PhD

Ph.D, Biostatistics  Emory University,   2012

Master of Science in Public Health, Emory University

Master of Science, Mathematics and Statistics, East Tennessee State University   

Bachelor of Science, Electrical Engineering, Harbin Institute of Technology, Harbin, China  

Dr. Chen is a biostatistician with a research focus on modeling large biomedical data with complex and organized structures. For example, he develops methods modeling the spatiotemporal dependence in neuroimaging data, linkage disequilibrium in genetics data, co-expression graph structure in omics data, and inference for edges in group-level graphs. Besides population-level statistical inference, he is also interested in individual-level inference by developing novel machine learning models that take into account the complex and organized dependence patterns between high-throughput features. Dr. Chen also has extensive experience in biostatistical collaborative research including clinical trial design and analysis, environmental health, infectious disease, and cancer research. 

Lab website: https://www.umdbright.com/

Big Data, machine learning, clinical trial design/analysis, network/graph statistics, neuroimaging statistics (MRI, EEG, fMRI, MRS, and DTI data), genomics and proteomics, Bayesian modeling, spatiotemporal models, and optimization.

# the corresponding author;

$  trainees and advisees of Chen

1.  Zhao, Z.^$, Chen, C.,  Adhikari, B., Kochunov, P., Elliot Hong, L., & Chen, S^#. (2023)Mediation Analysis for High-Dimensional Mediators and Outcomes with an Application to Multimodal Imaging Data. Computational Statistics and Data Analysis. in press.
2. Chen, S^#., Zhang, Y., Wu, Q., Bi, C., Kochunov, P., & Hong, L. E. (2023). Identifying covariate-related subnetworks for whole-brain connectome analysis. Biostatistics, in press.
3. Chen, C., Wang, M., & Chen, S.. (2023). An efficient data integration scheme for synthesizing information from multiple secondary datasets for the parameter inference of the main analysis. Biometrics, in press.
4. Adhikari, B. M., Hong, L. E., Zhao, Z., Wang, D. J., Thompson, P. M., Jahanshad, N., ...  Chen, S.., Kochunov, P. (2022). Cerebral blood flow and cardiovascular risk effects on resting brain regional homogeneity. NeuroImage, 262, 119555.
5. Mo C^$., Wang J^$., Ye Z^$., Ke H., Liu S., Hatch K., Gao S., Magidson J., Chen C., Mitchell BD., Kochunov P., Hong L.E., Ma, T.^#, and Chen, S^#(2022). Evaluating the causal effect of tobacco smoking on white matter brain aging: a two-sample Mendelian randomization analysis in UK Biobank. Addiction, in press.
6. Lee, H.^$, Chen, C., Kochunov, P., Elliot Hong, L., & Chen, S^#.(2022)  Modeling multivariate age‐related imaging variables with dependencies. Statistics in Medicine, in press.
7. Ge, Y.^$, Chen, G., Waltz, J. A., Kochunov, P., Elliot Hong, L., & Chen, S^#. (2022) Bayes estimate of primary threshold in clusterwise functional magnetic resonance imaging inferences. Human Brain Mapping, in press.
8. Mo, C^$., Ye, Z^$., Ke, H., Lu, T., Canida, T., Liu, S., ... & Chen, S^#. (2022). A new Mendelian Randomization method to estimate causal effects of multivariable brain imaging exposures. In PACIFIC SYMPOSIUM ON BIOCOMPUTING 2022 (pp. 73-84).
9. Ke, H., Ren, Z., Qi, J., Chen, S., Tseng, G. C., Ye, Z., & Ma, T. (2022). High-dimension to high-dimension screening for detecting genome-wide epigenetic and noncoding RNA regulators of gene expression. Bioinformatics, 38(17), 4078-4087.
10. Ye, Z^$., Mo, C^$., Ke, H., Yan, Q., Chen, C., Kochunov, P., ... Chen, S^# & Ma, T^#. (2022). Meta-analysis of transcriptome-wide association studies across 13 brain tissues identified novel clusters of genes associated with nicotine addiction. Genes, 13(1), 37.
11. Wu Q^$, Huang, X., Culbreth, A., Waltz, J., Hong, L. E., & Chen, S^# (2021+). Extracting Brain Disease-Related Connectome Subgraphs by Adaptive Dense Subgraph Discovery. Biometrics, in press.
12.  Ge, Y.^$, Hare, S., Chen, G., Waltz, J. A., Kochunov, P., Elliot Hong, L., & Chen, S^#. (2021)Bayes estimate of primary threshold in clusterwise functional magnetic resonance imaging inferences. Statistics in Medicine. 40(25), 5673-5689.
13. Wang J$, Kochunov P, Sampath H, Hatch KS, Ryan MC, Xue F, Neda J, Paul T, Hahn B, Gold J, Waltz J, Hong LE, Chen, S^#. (2021)White matter brain aging in relationship to schizophrenia and its cognitive deficit. Schizophr Res. 2021 Mar 2;230:9-16. doi: 10.1016/j.schres.2021.02.003. [Epub ahead of print] PubMed PMID: 33667860. 230, 9-16
14. Wu Q^$, Zhang Z, Ma T, Waltz J, Milton D, Chen, S^#. (2021) Link Predictions for Incomplete Network Data with Outcome Misclassification, Statistics in Medicine. 40(6), 1519-1534.
15. Wu Q^$, Ma T, Liu Q, Milton D, Zhang Y, Chen, S^#. (2021) Extracting Interconnected Communities in Gene Co-expression Networks. Bioinformatics. 37(14), 1997-2003.
16.  Chen, S^#., Bowman, FD., Xing, Y^$.,  (2020). Detecting and Testing Altered Brain Connectivity Networks with K-partite Network Topology.   Computational Statistics and Data Analysis, 141, 109-122
17. Chen, S^#., Xing, Y^$., Kang, J.,  Kochunov P., Hong, E.L. (2020). Bayesian Modeling of Dependence in Brain Connectivity Data.   Biostatistics, 21 (2), 269-286
18.  Chen, S^#., Kang, J., Xing, Y^$., Zhao, Y., Milton, D. (2018). Estimating Large Covariance Matrix with Network Topology for High-dimensional Biomedical Data.   Computational Statistics and Data Analysis,  127, 82-95.
19. Chen S^#,  Bowman  F.D.,  and  Mayberg  H.  (2016).   A Bayesian  Hierarchical  Framework  for Modeling  Brain Connectivity for Neuroimaging  Data,  Biometrics, 72. 2.  . DOI: 10.1111/biom.12433
20. Chen S^#,  Kang J, Xing Y^$, and Wang G^$,  (2015). A parsimonious statistical method to detect group-wise differentially expressed functional connectivity networks,  Human Brain Mapping, 36(12), 5196-5206. DOI: 10.1002/hbm.23007