Ph.D. in Biostatistics, Emory University, 2012

M.S. in Mathematics and Statistics, East Tennessee State University

Bachelor in Electrical Engineering, Harbin Institute of Technology

Dr. Chen is an MPower Professor of Biostatistics and Bioinformatics. His research focuses on 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 and Former Trainees sections below for recent awards won by trainees and alumni job placements).

Dr. Chen also serves as:

• Director of Biostatistics and Data Science, Institute of Health Computing, UMSOM;
• Director of Biostatistics and Data Science, Maryland Psychiatric Research Center, UMSOM;
• Graduate Director of Ph.D. in Biostatistics and Medical Data Science, UMSOM
• Faculty member (Ph.D. student advisor), Applied Mathematics & Statistics, and Scientific Computation (AMSC), University of Maryland, College Park
• Affiliate faculty member, School of Public Health, University of Maryland, College Park

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

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

# = corresponding author;
$ = trainees and advisees of Chen

1. Lee, H^$.,  & Chen, S^#. (2025). Systematic Bias of Machine Learning Regression Models and Correction, IEEE Transactions on Pattern Analysis and Machine Intelligence (Impact factor 20.8, top journal in ML/AI), in press, https://ieeexplore.ieee.org/abstract/document/10930829
2. Lu, T^$., Zhang, Y., Lyzinski, V., Bi, C., Kochunov, P., Hong, E., & Chen, S^#. (2025). Evaluating the effects of high-throughput structural neuroimaging predictors on whole-brain functional connectome outcomes via network-based vector-on-matrix regression. Biometrics, in press.
3. Feng, L., Ye, Z., Pan, Y., McCoy, R. G., Mitchell, B. D., Kochunov, P., ... & Chen, S^# Ma, T^#. (2025). Adherence to life’s essential 8 is associated with delayed white matter aging. EBioMedicine, 115.
4. Lu, T^$., Chen, C., Huang, H. H., Kochunov, P., Hong, E., & Chen, S^#. (2025). Multiple Imputation Method for High-Dimensional Neuroimaging Data. Human Brain Mapping, in press.
5. Lee, H^$., Chen, C., Kochunov, P., Elliot Hong, L., & Chen, S^#. (2024). A new multiple-mediator model maximally uncovering the mediation pathway: evaluating the role of neuroimaging measures in age-related cognitive decline. Annals of Applied Statistics,  18 (4), 2775-2795.
6. Wu, Q^$., Zhang, Y., Huang, X., Ma, T., Hong, L. E., Kochunov, P., & Chen, S^#. (2024). A multivariate to multivariate approach for voxel-wise genome-wide association analysis. Statistics in Medicine, 43(20), 3862-3880.
7. Lu, T^$., Zhang, Y., Kochunov, P., Hong, E., & Chen, S^# (2024). Network method for voxel-pair-level brain connectivity analysis under spatial-contiguity constraints. Annals of Applied Statistics, 18 (2), 1090-1112
8. Chen, S^#., Zhang, Y., Wu, Q., Bi, C., Kochunov, P., & Hong, L. E. (2024). Identifying covariate-related subnetworks for whole-brain connectome analysis. Biostatistics, Biostatistics 25 (2), 541-558.
9. Lee, H^$., Ma, T., Ke, H., Ye, Z., Chen, S^# (2024) dCCA: detecting differential covariation patterns between two types of high-throughput omics data. Briefings in Bioinformatics, 25 (4), bbae288.
10. Pan, Y^$. Bi, C., Kochunov, P., Shardell, M., Smith, J. C., McCoy, R. G., ... & Chen, S^# (2024). Brain-wide functional connectome analysis of 40,000 individuals reveals brain networks that show aging effects in older adults. Imaging Neuroscience, 2, 1-15.
11. Pan, Y^$., Hong, L. E., Acheson, A., Thompson, P. M., Jahanshad, N., Zhu, A. H., ... & Chen, S^#(2024). A site-wise reliability analysis of the ABCD diffusion fractional anisotropy and cortical thickness: impact of scanner platforms. Human Brain Mapping, 45(16), e70070.
12. Chen, C., Han, P., Chen, S., Shardell, M., & Qin, J. (2024). Integrating external summary information in the presence of prior probability shift: an application to assessing essential hypertension. Biometrics, 80(3), ujae090.
13. Pan, Y^$., Bi, C., Ye, Z., Lee, H., Yammine, L., Ma, T., Kochunov, P., Hong, E., Chen, S^#. (2024). Tobacco Smoking Functional Networks: A Whole-Brain Connectome Analysis in 24,539 Individuals, Nicotine & Tobacco Research, ntae256.
14. Ye, Z^$., Pan, Y^$., McCoy, R. G., Bi, C., Chen, M., Feng, L., ... & Ma^#, T. Chen, S^#. (2024). Contrasting association pattern of plasma low-density lipoprotein with white matter integrity in APOE4 carriers versus non-carriers. Neurobiology of Aging, 143, 41-52
15. Bi, C.^$, Nichols, T., Lee, H., Yang, Y., Ye, Z., Pan, Y., Hong, L., Kochunov, P., and Chen, S^#, (2024) BNPower: a power calculation tool for data-driven network analysis for whole-brain connectome data, Imaging Neuroscience, in press.
16. Yang, Y.^$, Chen, C., & Chen, S^#. (2024). Covariance Matrix Estimation for High-Throughput Biomedical Data with Interconnected Communities. The American Statistician, in press.
17. 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.
18. Mo, C.^$, Ye, Z.^$, Pan, Y.^$, Zhang, Y., Wu, Q., Bi, C., ... & Chen, S^#. (2023). An in-depth association analysis of genetic variants within nicotine-related loci: Meeting in middle of GWAS and genetic fine-mapping. Molecular and Cellular Neuroscience, 103895.
19. Zhao, Z.^$, 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. 185, 107765.
20. 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, 79(4), 2947-2960.
21. Ye, Z.,^$ Mo, C.^$, Liu, S., Gao, S., Feng, L., Zhao, B., ... & Chen, S^#, Ma, T^#. (2023). Deciphering the causal relationship between blood pressure and regional white matter integrity: A two‐sample Mendelian randomization study. Journal of Neuroscience Research, 101(9), 1471-1483.
22. Chen, C., Chen, S., Long, Q., Das, S., & Wang, M. (2023). Multiple-model-based robust estimation of causal treatment effect on a binary outcome with integrated information from secondary outcomes. The American Statistician, 1-21.
23. Feng, L., Ye, Z., Mo, C., Wang, J., Liu, S., Gao, S., ... & Chen, S^# , Ma, T^#.(2023). Elevated blood pressure accelerates white matter brain aging among late middle-aged women: a Mendelian Randomization study in the UK Biobank. Journal of Hypertension, in press.
24. Adhikari, B. M., Hong, L. E., Zhao, Z., Wang, D. J., Thompson, P. M., Jahanshad, N., ... Chen, S.., Kochunov, P. (2022). Cerebral blood flow and cardiovascular risk effects on resting brain regional homogeneity. NeuroImage, 262, 119555.
25. Mo C^$., 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, 118(4), 739-749.
26. Lee, H.^$, Chen, C., Kochunov, P., Elliot Hong, L., & Chen, S^#.(2022) Modeling multivariate age‐related imaging variables with dependencies. Statistics in Medicine, in press.
27. Ge, Y.^$, 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, 43(8), 2444-2459.
28. 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).
29. 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.
30. 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.
31. Wu Q^$, Huang, X., Culbreth, A., Waltz, J., Hong, L. E., & Chen, S^# (2022). Extracting Brain Disease-Related Connectome Subgraphs by Adaptive Dense Subgraph Discovery. Biometrics, 78(4), 1566-1578.
32. Ge, Y.^$, 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.
33. Wang J$, Kochunov P, Sampath H, Hatch KS, Ryan MC, Xue F, Neda J, Paul T, Hahn B, Gold J, Waltz J, Hong LE, Chen, S^#. (2021) White matter brain aging in relationship to schizophrenia and its cognitive deficit. Schizophr Res. 2021 Mar 2;230:9-16. doi: 10.1016/j.schres.2021.02.003. [Epub ahead of print] PubMed PMID: 33667860. 230, 9-16
34. Wu Q^$, 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.
35. Wu Q^$, Ma T, Liu Q, Milton D, Zhang Y, Chen, S^#. (2021) Extracting Interconnected Communities in Gene Co-expression Networks. Bioinformatics. 37(14), 1997-2003.
36. Chen, S^#., Bowman, FD., Xing, Y^$., (2020). Detecting and Testing Altered Brain Connectivity Networks with K-partite Network Topology. Computational Statistics and Data Analysis, 141, 109-122
37. Chen, S^#., Xing, Y^$., Kang, J., Kochunov P., Hong, E.L. (2020). Bayesian Modeling of Dependence in Brain Connectivity Data. Biostatistics, 21 (2), 269-286
38. Chen, S^#., Kang, J., Xing, Y^$., 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.
39. 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
40. 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