Hegang Chen

Hegang Chen, PhD, serves as Professor in the Department of Epidemiology and Public Health at the University of Maryland School of Medicine. He is a lead statistician for the General Clinical Research Center (GCRC) and member of the Hormone Responsive Cancers Program within the Marlene and Stewart Greenebaum Cancer Center, with extensive collaborative experience in clinical trials and epidemiological research since joining in 2002.

His academic credentials include:

• Ph.D. in Statistics from the University of Illinois
• M.S. in Mathematics from the University of Mississippi

Dr. Chen's research centers on statistical methodology development—including optimal experimental design, generalized mixed linear models, and machine learning applications for molecular biology and real-time clinical decision support (e.g., predicting blood transfusion needs)—and biomedical collaborations spanning cancer, infectious diseases, trauma, public health, and pharmacogenomics. His work has been published in premier journals like Nature, JAMA, and Annals of Statistics.

Analysis of his recent publications (2020-2025) reveals a dominant focus on predictive analytics for traumatic brain injury outcomes, blood transfusion prediction, and lung cancer diagnosis using real-time physiological monitoring and machine learning. This trend demonstrates a translational shift toward operationalizing statistical methods in critical care decision support systems.

No scientific awards were mentioned in the provided text.

While no specific advisees are listed, Dr. Chen's leadership in the GCRC and extensive collaborative network across trauma, oncology, and global health indicate active mentorship within multidisciplinary teams. His grant involvement is evidenced by NIH-funded GCRC work and high-impact publications in clinical domains.

Dr. Chen maintains key affiliations with the General Clinical Research Center as lead statistician and the Hormone Responsive Cancers Program, where he integrates advanced statistical methodologies into translational cancer research and clinical trial design.