Georgetown University, Department of Biostatistics, Bioinformatics and Biomathematics

The Department of Biostatistics, Bioinformatics and Biomathematics is part of Georgetown University Medical Center (GUMC). It offers a MS degree in Biostatistics with tracks in Bioinformatics and Epidemiology, and Certificates in Biostatistics and Epidemiology. These educational programs provide integrated training in computational, quantitative, and biomedical sciences to support health-related research performed in academia, government, and industry. Students not only acquire the quantitative and computational tools that underpin epidemiology and bioinformatics, but also gain substantive exposure to applications of these tools to biological and health sciences.

The Department houses the Biostatistics and Bioinformatics Shared Resource (BBSR) of the Lombardi Comprehensive Cancer Center (LCCC). The primary objectives of the BBSR are: (i) to collaborate with LCCC investigators on the biostatistics and bioinformatics aspects of basic science, clinical, and population science research projects, (ii) to participate effectively in the clinical trials program by providing biostatistics input to the planning and conduct of all LCCC clinical trials, by providing biostatistical reviews of proposed protocols, by active membership on the Clinical Research Committee, and by the monitoring of all LCCC trials through the Data and Safety Monitoring Committee, (iii) to educate LCCC investigators, staff and students in
biostatistics and bioinformatics methodology for the planning, conduct, analysis and interpretation of cancer research studies, (iv) to perform research in biostatistics and bioinformatics methodology on problems arising from collaborations with investigators on cancer research projects; and (v) to coordinate with GUMC Biomedical Informatics Centers, and to implement a common user interface for all LCCC shared databases.

Faculty research spans a wide array of areas: computational methods for genomics, proteomics and metabolomics, statistical methods for flow cytometry data, survival analysis, empirical likelihood methodology, two-stage designs for clinical trials, database integration, and text mining.