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Health and Biomedical Data Science- PHD

Overview


NEW 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.

 

 


Admissions


Applicants must hold an undergraduate degree from an accredited institution of higher education, exhibiting a strong background in mathematics. Typically, a GPA of B or higher from an accredited college is expected. With evidence of exceptional promise, such as high Graduate Record Examination (GRE) scores and research experience, an applicant whose academic record falls short of a B average may be considered. All applicants are required to submit current GRE scores (within five years of matriculation date).

Meeting the minimum requirements does not assure acceptance. Applicants must provide evidence of the completion of their undergraduate and/or graduate work before registration in GWSPH is permitted.

 

Concentration-Specific Prerequisites

   Applied Biostatistics 

  Applied Bioinformatics 

  • Three semesters of calculus (through multivariable calculus)
  • A course in linear algebra
  • A course in undergraduate statistics

Additional advanced courses in mathematics and calculus-based probability are encouraged but not a requirement for admission.

  • A course in statistics
  • A course in introductory biology and/or a course in computer programming
  • Typically, an undergraduate major in either biology, statistics, mathematics, computer science, bioinformatics, and/or bioengineering

 

Transfer Credits

Graduate courses taken prior to admission while in non-degree status may not be transferable into GWSPH programs. The PhD program is designed to serve students coming directly from an undergraduate degree. Students completing a master’s degree prior to admission to the PhD degree program may be eligible to transfer up to 24 credits toward the PhD coursework requirements. Depending on how many transfer credits are accepted, at minimum, 48 credits of additional coursework and dissertation research will be required.


Curriculum


PhD Core Requirements

PUBH 6080 | Pathways to Public Health (0 credits)
PUBH 6421 | Responsible Conduct of Research (1 credit)
PUBH 6850 | Introduction to SAS for Public Health Research (1 credit)
PUBH 6851 | Introduction to R for Public Health Research (1 credit)
PUBH 6852 | Introduction to Python for Public Health Research (1 credit) 
PUBH 6860 | Principles of Bioinformatics (3 credits) 
PUBH 6886 | Statistical and Machine Learning for Public Health and Biomedical Research (3 credits)
PUBH 8099 | PhD Seminar: Cross Cutting Issues in Public Health (1 credit)
PUBH 8870 | Statistical Inference for Public Health Research I (3 credits)
 

CORE TOTAL: 14 CREDITS

 

SPH Course Descriptions

Applied Biostatistics Concentration

PUBH 6866 | Principles of Clinical Trials (3 credits) 
PUBH 6869 | Principles of  Biostatistical Consulting (1 credit)
PUBH 6879 | Propensity Score Methods for Causal Inference in Observational Studies (3 credits)
PUBH 6887 | Applied Longitudinal Data Analysis (3 credits)
PUBH 8871 | Statistical Inference for Public Health Research II (3 credits)
PUBH 8875 | Linear Models in Biostatistics (3 credits) 
PUBH 8877 | Generalized Linear Models in Biostatistics (3 credits)
PUBH 8878 | Statistical Genetics (3 credits)
PUBH 8880 | Statistical Computing (3 credits)
STAT 6227  | Survival Analysis (3 credits)

 

SPH Course Descriptions

APPLIED BIOSTATISTICS CONCENTRATION-SPECIFIC TOTAL: 28 CREDITS

 

Applied Bioinformatics Concentration

PUBH 6859 | High Performance Cloud Computing (3 credits) 
PUBH 6861 | Public Health Genomics (3 credits) 
PUBH 6884 |Bioinformatics  Algorithms and Data Structure (3 credits) 
PUBH 6885 | Computational Biology (3 credits) 

SPH Course Descriptions

APPLIED BIOINFORMATICS CONCENTRATION-SPECIFIC TOTAL: 12 CREDITS

 

Electives

  • Applied Biostatistics Concentration: 12 credits minimum
  • Applied Bioinformatics Concentration:  21 credits minimum

Both concentrations:  elective selections must include at least*:

  • 3 Credits in Biostatistics 
  • 3 Credits in Bioinformatics or Data Science 
  • 3 Credits in a Cognate Area

All students are expected to work with their Advisor in the selection of their Elective coursework.

*Pre-approved elective courses are shown in the program guide for each category. 

 APPLIED BIOSTATISTICS ELECTIVES MINIMUM: 12 CREDITS
APPLIED BIOINFORMATICS ELECTIVES MINIMUM: 21 CREDITS

 

 

Practicum/Teaching/Research

Univ 0250   | University Graduate Training Assistantship  (Both Concentrations)- (0 credits)
PUBH 8283 | Doctoral Biostatistical Consulting Practicum (Biostatistics Concentrations ONLY)- (2 credits)
PUBH 6897 | Readership in Biostatistics and Bioinformatics (Both Concentrations)- (1 credit)
PUBH 8999 | Dissertation Research (varies by concentration- 12 minimum credits)

APPLIED BIOSTATISTICS PRACTICUM/RESEARCH: 15-18 CREDITS
APPLIED BIOINFORMATICS PRACTICUM/RESEARCH: 13-25 CREDITS

 

 

 

Non-Academic Requirements

Professional Enhancement

Students in degree programs must participate in eight hours of Professional Enhancement. These activities may be Public Health-related lectures, seminars, or symposia related to your field of study.

Professional Enhancement activities supplement the rigorous academic curriculum of the SPH degree programs and help prepare students to participate actively in the professional community. You can learn more about opportunities for Professional Enhancement via the Milken Institute School of Public Health Listserv, through departmental communications, or by speaking with your advisor.

Students must submit a completed Professional Enhancement Form to the student records department gwsphrecords@gwu.edu.

Collaborative Institutional Training Initiative (CITI) Training

All students are required to complete the Basic CITI training module in Social and Behavioral Research prior to beginning the practicum.  This online training module for Social and Behavioral Researchers will help new students demonstrate and maintain sufficient knowledge of the ethical principles and regulatory requirements for protecting human subjects - key for any public health research.

Academic Integrity Quiz

All Milken Institute School of Public Health students are required to review the University’s Code of Academic Integrity and complete the GW Academic Integrity Activity.  This activity must be completed within 2 weeks of matriculation. Information on GWSPH Academic Integrity requirements can be found here.

 

 

**For graduation requirements, please download the program guide.**


Faculty


Students pursuing a PhD in Health and Biomedical Data Science have access to a world-class faculty with relevant expertise and diverse experience in all sectors of public health and medical research. Areas of interest and research experience for professors and lecturers in the program include: clinical trials, statistical modeling, machine learning, computing and software development, survival analysis, and finite population sampling, with applications in infectious diseases (including COVID-19, HIV, and bacterial superbug infections), mental health, diabetes, maternal-fetal medicine, and cardiovascular disease.  Learn about the Department of Biostatistics and Bioinformatics faculty here.

 

 


Program Co-Directors

Keith Crandall (Applied Bioinformatics)

Yan Ma (Applied Biostatistics)

Apply Program Guide