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Shuo Chen, PhD

Academic Title:

Professor

Primary Appointment:

Epidemiology & Public Health

Secondary Appointment(s):

Psychiatry

Additional Title:

Director of Biostatistics and Data Science, Institute of Health Computing, UMSOM; Director of Biostatistics and Data Science, Maryland Psychiatric Research Center, UMSOM; Full (inter-campus) faculty member (Ph.D. student advisor), Applied Mathematics & Statistics, and Scientific Computation (AMSC), University of Maryland, College Park

Location:

MPRC

Phone (Primary):

410-402-7141

Education and Training

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  

Biosketch

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.

Dr. Chen is also enthusiastic about fostering the next generation of statisticians and data scientists (see the Awards section below for recent awards won by trainees). 

 

 

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

Research/Clinical Keywords

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.

Highlighted Publications

# the corresponding author;

$  trainees and advisees of Chen

 

 

  1. Bi, C.$, Nichols, T., Lee, H., Yang, Y., Ye, Z., Pan, Y., Hong, L., Kochunov, P., and Chen, S#, (2024) BNPower: a power calculation tool for data-driven network analysis for whole-brain connectome data, Imaging Neuroscience, in press. 
  2. Yang, Y.$, Chen, C., & Chen, S#. (2024). Covariance Matrix Estimation for High-Throughput Biomedical Data with Interconnected Communities.  The American Statistician, in press.
  3. Tian, C.$, Ye, Z.$, McCoy, R. G., Pan, Y., Bi, C., Gao, S., ...  Chen, S# and Liu, S  (2024). The causal effect of HbA1c on white matter brain aging by two-sample Mendelian randomization analysis. Frontiers in Neuroscience, 17.
  4. Lu, T$., Zhang, Y., Kochunov, P., Hong, E., & Chen, S# (2023). Network method for voxel-pair-level brain connectivity analysis under spatial-contiguity constraints. Annals of Applied Statistics, in press.
  5. Mo, C.$, 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.
  6. Zhao, Z.$, Chen, C.,  Adhikari, B., Kochunov, P., Elliot Hong, L., & Chen, S#. (2023)Mediation Analysis for High-Dimensional Mediators and Outcomes with an Application to Multimodal Imaging Data. Computational Statistics and Data Analysis. in press. 
  7. Chen, S#., Zhang, Y., Wu, Q., Bi, C., Kochunov, P., & Hong, L. E. (2023). Identifying covariate-related subnetworks for whole-brain connectome analysis. Biostatistics, in press.
  8. Chen, C., Wang, M., & Chen, S.. (2023). An efficient data integration scheme for synthesizing information from multiple secondary datasets for the parameter inference of the main analysis. Biometrics, in press.
  9. Ye, Z.,$ Mo, C.$, Liu, S., Gao, S., Feng, L., Zhao, B., ... &  Chen, S#, Ma, T#. (2023). Deciphering the causal relationship between blood pressure and regional white matter integrity: A two‐sample Mendelian randomization study. Journal of Neuroscience Research101(9), 1471-1483.
  10. Chen, C., Chen, S., Long, Q., Das, S., & Wang, M. (2023). Multiple-model-based robust estimation of causal treatment effect on a binary outcome with integrated information from secondary outcomes. The American Statistician, 1-21.
  11. Feng, L., Ye, Z., Mo, C., Wang, J., Liu, S., Gao, S., ... &  Chen, S# , Ma, T#.(2023). Elevated blood pressure accelerates white matter brain aging among late middle-aged women: a Mendelian Randomization study in the UK Biobank. Journal of Hypertension, in press.
  12. Adhikari, B. M., Hong, L. E., Zhao, Z., Wang, D. J., Thompson, P. M., Jahanshad, N., ...  Chen, S.., Kochunov, P. (2022). Cerebral blood flow and cardiovascular risk effects on resting brain regional homogeneity. NeuroImage, 262, 119555.
  13. Mo C$., 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.
  14. Lee, H.$, Chen, C., Kochunov, P., Elliot Hong, L., & Chen, S#.(2022)  Modeling multivariate age‐related imaging variables with dependencies. Statistics in Medicine, in press.
  15. Ge, Y.$, 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.
  16. 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).
  17. 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. Bioinformatics38(17), 4078-4087.
  18. 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. Genes13(1), 37.
  19. Wu Q$, Huang, X., Culbreth, A., Waltz, J., Hong, L. E., & Chen, S# (2021+). Extracting Brain Disease-Related Connectome Subgraphs by Adaptive Dense Subgraph Discovery. Biometrics, in press.
  20.  Ge, Y.$, 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 Medicine40(25), 5673-5689.
  21. Wang J$, Kochunov P, Sampath H, Hatch KS, Ryan MC, Xue F, Neda J, Paul T, Hahn B, Gold J, Waltz J, Hong LE, Chen, S#. (2021)White matter brain aging in relationship to schizophrenia and its cognitive deficit. Schizophr Res2021 Mar 2;230:9-16doi: 10.1016/j.schres.2021.02.003. [Epub ahead of print] PubMed PMID: 33667860230, 9-16
  22. Wu Q$, 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.
  23. Wu Q$, Ma T, Liu Q, Milton D, Zhang Y, Chen, S#. (2021) Extracting Interconnected Communities in Gene Co-expression Networks. Bioinformatics37(14), 1997-2003.
  24.  Chen, S#., Bowman, FD., Xing, Y$.,  (2020). Detecting and Testing Altered Brain Connectivity Networks with K-partite Network Topology.   Computational Statistics and Data Analysis, 141, 109-122
  25. Chen, S#., Xing, Y$., Kang, J.,  Kochunov P., Hong, E.L. (2020). Bayesian Modeling of Dependence in Brain Connectivity Data.   Biostatistics, 21 (2), 269-286
  26.  Chen, S#., Kang, J., Xing, Y$., 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.
  27. 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
  28. 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

 

Additional Publication Citations

Awards and Affiliations

Selected trainee/advisee awards (advised by Dr. Chen)

2024  ENAR  Distinguished Student Paper Award

Title: Modeling Multivariate Outcomes with Dependence Structures of Interconnected Modules: Evaluating the Effect of Alcohol Intake on Plasma Metabolomics (Yifan Yang)

2024  JSM Student Paper Competition Runner-up Winner, American Statistical Association, Statistical Imaging Section, JSM, 2024.

Title: Evaluating the effects of high-throughput structural neuroimaging predictors on whole-brain functional connectome outcomes via network-based vector-on-matrix regression  (Tong Lu)

2020 JSM  Student Paper Competition First Place Winner, American Statistical Association, Statistical Imaging Section, JSM, 2020.

Title: Extracting Brain Disease-Related Connectome Subgraphs by Adaptive Dense Subgraph Discovery (Qiong Wu)

2020   SMI Student Paper Competition First Place Winner, The Statistical Methods in Imaging Conference 2020.

Title: Predicting Latent Links from Incomplete Network Data Using Exponential Random Graph Model with Outcome Misclassification (Qiong Wu)

 

Awards (Dr. Chen)

2019   Avenir Award, National Institute of Health, USA.

2012   Young Investigator Award Competition Travel Award, American Statistical Association, Biometrics Section, JSM 2012.

2011   Statistical Learning and Data Mining Student Paper Competition winner, American Statistical Association, JSM 2011.

2011   Livingston Fellowship.

2011   NIH Biostatistics in Genetics, Immunology, and Neuroimaging (BGIN) Training Program Fellowship, 2011-2012.