We are a group of biostatisticians and bioinformaticians from the University of Maryland. Our research has focused on modeling large and complex biomedical data such as imaging and genomic data generated by modern high-throughput technologies. We aim to develop effective and impactful methodologies to have better insight of the disease mechanism and improve the related health outcomes. We are actively seeking for collaboration with biologists, clinicians, epidemiologists and researchers in all relevant fields.



  • May 2021, Congratulations to Yunjiang Ge for winning the Hauptman Fellowship for Summer 2021.

  • March 2021, Congratulations to Charles and Shuo for winning the MPower BHHP seed grant.  

  • August 2020, Zhenyao Ye formally joined us as a research data scientist at UMSOM, welcome Zhenyao!

  • July 2020, Congratulations to Qiong Wu for winning the First Place Winner of the 2020 Student Paper Competition of American Statistical Association (ASA)  Statistics in Imaging Section. Title: "Extracting Brain Disease-Related Connectome Subgraphs by Adaptive Dense Subgraph Discovery".

  • May 2020, Congratulations to Qiong Wu for winning Student Paper Competition Award at 2020 Statistical Methods in Imaging (SMI) conference. Title: "Link Predictions for Incomplete Network Data with Outcome Misclassification".

  • January 2020, Chen Mo joined us as a research fellow, welcome Chen!

  • December 2019, Congratulations to Qiong Wu for winning the Travel Award to the Fourteenth Annual Workshop for Women in Machine Learning (WiML2019, co-located with NeurIPS 2019.

  • December 2019, Congratulations to Charles for winning the UMD FSRA award. 

  • September 2019, Congratulations to Shuo for winning the NIH Avenir award.

  • July 2019, Congratulations to Qiong Wu for the Student Travel Award Big Data Neuroscience Workshop 2019 -




Developing statistical models to handle the high-dimensional imaging variables with a complex  covariance structure that is related to the spatiotemporal information for the group-level association analysis and inference. Developing machine learning models for individual-level diagnosis and prognosis.   



Developing rigorous and impactful statistical and bioinformatics methods tailored to the analysis of high-throughput genetic and genomic data in biomedical, epidemiological and clinical studies. for biomarker discovery, prognosis research and network analysis, etc.


Developing novel meta-analysis and integrative analytic methods to jointly analyze multimodal omics and imaging data to unravel the disease mechanism and ultimately inspire new approaches for the prevention and treatment of disease. Specific fields of interest include imaging genetics research for brain-related diseases, multi-level omics data integration in cancer application.