Epidemiology & Public Health
Director of Biostatistics and Data Science, at Maryland Psychiatric Research Center
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
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 imaging 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, optimization.
# the corresponding author;
$ trainees and advisees of Chen
- 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. in press
- Wu Q$, Zhang Z, Ma T, Waltz J, Milton D, Chen, S#. (2021+) Link Predictions for Incomplete Network Data with Outcome Misclassification, Statistics in Medicine. in press
- Wu Q$, Ma T, Liu Q, Milton D, Zhang Y, Chen, S#. (2021+) Extracting Interconnected Communities in Gene Co-expression Networks. Bioinformatics. in press.
- Chen, S#., Bowman, FD., Xing, Y$., (2020). Detecting and Testing Altered Brain Connectivity Networks with K-partite Network Topology. Computational Statistics and Data Analysis, 141, 109-122
- Chen, S#., Xing, Y$., Kang, J., Kochunov P., Hong, E.L. (2020). Bayesian Modeling of Dependence in Brain Connectivity Data. Biostatistics, 21 (2), 269-286
- Chen, S#., Kang, J., Xing, Y$., Zhao, Y., Milton, D. (2018). Estimating Large Covariance Matrix with Network Topology for High-dimensional Biomedical Data. Computational Statistics and Data Analysis, 127, 82-95.
- Chen S# , Xing Y$ and Kang J (2017). Latent and Abnormal Functional Connectivity Circuits in Autism Spectrum Disorders. Front. Neurosci. 11:125. doi: 10.3389/fnins.2017.00125
- 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
- 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
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.
Selected trainee/advisee award
2020 Advisor of graduate student Qiong Wu: 2020 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
2020 Advisor of graduate student Qiong Wu: 2020 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