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 specializing in modeling complex structured biomedical data, including spatiotemporal dependence in neuroimaging, linkage disequilibrium in genetics, co-expression graph structures in omics, and group-level graph edge inference. He is also involved in developing machine learning models for individual-level inference, considering complex dependencies between high-throughput features. Dr. Chen has broad experience in collaborative biostatistical research, including clinical trials, 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.  Lu, T^$., Zhang, Y., Kochunov, P., Hong, E., & Chen, S^#(2023). Network method for voxel-pair-level brain connectivity analysis under spatial-contiguity constraints. Annals of Applied Statistics, in press.
2. Mo, C.^$, Ye, Z.^$, Pan, Y.^$, Zhang, Y., Wu, Q., Bi, C., ... & Chen, S^#. (2023). An in-depth association analysis of genetic variants within nicotine-related loci: Meeting in middle of GWAS and genetic fine-mapping. Molecular and Cellular Neuroscience, 103895.
3. Zhao, Z.^$, Chen, C.,  Adhikari, B., Kochunov, P., Elliot Hong, L., & Chen, S^#. (2023)Mediation Analysis for High-Dimensional Mediators and Outcomes with an Application to Multimodal Imaging Data. Computational Statistics and Data Analysis. in press. 
4. Chen, S^#., Zhang, Y., Wu, Q., Bi, C., Kochunov, P., & Hong, L. E. (2023). Identifying covariate-related subnetworks for whole-brain connectome analysis. Biostatistics, in press.
5. Ye, Z.,^$ Mo, C.^$, Liu, S., Gao, S., Feng, L., Zhao, B., ... & Chen, S^# Ma, T^#. (2023). Deciphering the causal relationship between blood pressure and regional white matter integrity: A two‐sample Mendelian randomization study. Journal of Neuroscience Research, 101(9), 1471-1483.
6. Chen, C., Chen, S., Long, Q., Das, S., & Wang, M. (2023). Multiple-model-based robust estimation of causal treatment effect on a binary outcome with integrated information from secondary outcomes. The American Statistician, 1-21.
7. Feng, L., Ye, Z., Mo, C., Wang, J., Liu, S., Gao, S., ... & Chen, S^# Ma, T^#.(2023). Elevated blood pressure accelerates white matter brain aging among late middle-aged women: a Mendelian Randomization study in the UK Biobank. Journal of Hypertension, in press.
8. Chen, C., Wang, M., & Chen, S.. (2023). An efficient data integration scheme for synthesizing information from multiple secondary datasets for the parameter inference of the main analysis. Biometrics, in press.
9. Adhikari, B. M., Hong, L. E., Zhao, Z., Wang, D. J., Thompson, P. M., Jahanshad, N., ...  Chen, S.., Kochunov, P. (2022). Cerebral blood flow and cardiovascular risk effects on resting brain regional homogeneity. NeuroImage, 262, 119555.
10. Mo C^$., 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.
11. Lee, H.^$, Chen, C., Kochunov, P., Elliot Hong, L., & Chen, S^#.(2022)  Modeling multivariate age‐related imaging variables with dependencies. Statistics in Medicine, in press.
12. Ge, Y.^$, 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.
13. 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).
14. 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.
15. 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.
16. Wu Q^$, Huang, X., Culbreth, A., Waltz, J., Hong, L. E., & Chen, S^# (2021+). Extracting Brain Disease-Related Connectome Subgraphs by Adaptive Dense Subgraph Discovery. Biometrics, in press.
17.  Ge, Y.^$, 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.
18. Wang J$, Kochunov P, Sampath H, Hatch KS, Ryan MC, Xue F, Neda J, Paul T, Hahn B, Gold J, Waltz J, Hong LE, Chen, S^#. (2021)White matter brain aging in relationship to schizophrenia and its cognitive deficit. Schizophr Res. 2021 Mar 2;230:9-16. doi: 10.1016/j.schres.2021.02.003. [Epub ahead of print] PubMed PMID: 33667860. 230, 9-16
19. Wu Q^$, 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.
20. Wu Q^$, Ma T, Liu Q, Milton D, Zhang Y, Chen, S^#. (2021) Extracting Interconnected Communities in Gene Co-expression Networks. Bioinformatics. 37(14), 1997-2003.
21.  Chen, S^#., Bowman, FD., Xing, Y^$.,  (2020). Detecting and Testing Altered Brain Connectivity Networks with K-partite Network Topology.   Computational Statistics and Data Analysis, 141, 109-122
22. Chen, S^#., Xing, Y^$., Kang, J.,  Kochunov P., Hong, E.L. (2020). Bayesian Modeling of Dependence in Brain Connectivity Data.   Biostatistics, 21 (2), 269-286
23.  Chen, S^#., Kang, J., Xing, Y^$., 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.
24. 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
25. 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