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Key characteristics of program:
The PhD Program in Health and Biomedical Data Science trains the next generation of data science leaders for applications in public health and medicine. The program advances future leaders in health and biomedical data science by: (i) providing rigorous training in the fundamentals of health and biomedical data science, (ii) fostering innovative thinking for the design, conduct, analysis, and reporting of public health research studies, and (iii) providing practical training through real-world research opportunities at research centers and institutes directed by departmental faculty such as the Biostatistics Center (BSC), the Computational Biology Institute (CBI), and the Biostatistics and Epidemiology Consulting Service (BECS).
The PhD program consists of two concentrations; Applied Biostatistics & Applied Bioinformatics. Biostatistics is the science of designing, conducting, analyzing, and reporting studies aimed at advancing public health and medicine. Bioinformatics is the science of developing and applying computational algorithms and analysis methodologies to big biological data such as genetic sequences. Together they are foundational sciences for public health research and decision-making and essential to educating the next generation of leaders in health and biomedical data science.
The program takes advantage of the rich biostatistical and bioinformatics resources at GW and in the Nation’s Capital. Faculty in the Department of Biostatistics and Bioinformatics are engaged in a diverse research portfolio that includes areas such as diabetes, infectious diseases, mental health, maternal-fetal medicine, cardiovascular disease, emergency medicine, and oncology. Methodological interests of the faculty include the design and analyses of clinical trials including group-sequential and adaptive design, SMART trials, pragmatic trials, multiple testing, and benefit: risk evaluation; machine learning; meta-analyses; missing data; randomization tests, longitudinal data; the use of real-world data including electronic medical records; and research in biostatistics education methodologies. The Washington DC area is a hub for biostatisticians and bioinformaticians in government and industry, providing a rich source of adjunct faculty with relevant experience. Specifically, the National Institutes of Health (NIH) and the Food and Drug Administration (FDA) have considerable human resources in these disciplines, many with world-class reputations. Several leading biostatisticians from the NIH are currently serving on doctoral committees and teach courses in the Milken Institute School of Public Health (GWSPH).
The program features a modernized applied curriculum, unique in its emphasis on cutting-edge data science techniques while retaining the rigor of traditional Biostatistics and Bioinformatics programs. The program prepares students to be independent researchers and effective collaborators in interdisciplinary studies.
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Program Goals:
The PhD in Health and Biomedical Data Science trains the next generation of data science leaders for applications in public health and medicine. Students in the program develop innovative statistical and computational methods for data analysis and for deriving new scientific discoveries in the public health and biomedical sciences. The program takes advantage of the rich biostatistical and bioinformatics resources at GWU and in the Nation’s Capital and is designed to prepare students to be independent researchers in the development of methodologies, and effective collaborators in interdisciplinary studies.
Applied Bioinformatics Concentration Competencies 1. Conceptual Integration and Application in Bioinformatics: Students will integrate concepts and data across fields of computer science, statistics, data science, biology, and health sciences through bioinformatics. Through such integration, they will gain project management skills, collaborative ability, critical thinking, and leadership skills. 2. Computation: Students will gain skills in programming, data structures, algorithms, machine learning, highperformance computing and apply these skills to create computer programs that facilitate biological data analysis. 3. Biology: Students will gain a basis of knowledge in molecular biology, genomics, genetics, evolutionary theory, systems biology and apply this knowledge through statistics and computation to address research questions. 4. Statistics and Mathematics: Students will develop basic skills and learn applications of statistical approaches and foundational mathematical principles and apply these to molecular biology and genomics through modeling and/or data analyses. 5. Foundational Knowledge in Bioinformatics: Students will gain an understanding of foundational concepts in bioinformatics, including genomics, algorithms, and experience with key tools in bioinformatics.
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