NIH-based training grants are becoming increasingly competitive, and thus it is critical that students appointed to them make a long-term commitment to carrying out research in the relevant subject area. All training grants have requirements which must be fulfilled to justify the financial support of the student (tuition, fees, and stipend).
For the Big Data training grant, these include the following:
Cognate: Trainees must complete their cognate in an area related to Big Data. The specific courses and theme should be developed by the student along with their advisor and the training grant director; all cognates must be approved as part of the final program by the Director of Graduate Studies. Trainees are expected to complete their cognate in an interdisciplinary area in which Big Data arise, such as genetics/genomics, caner, epidemiology, environmental science, infectious disease, social science, health management, and medical sciences.
Coursework: Trainees are required to complete three lab rotations under the course title BST 314 (each 2.5 credits), as described in greater detail below. In addition, students must complete the following courses: BIST 234 (or CS124) (Data Structures and Algorithms), BIST 235 (Advanced Regression and Statistical Learning), BST 261 (Data Science II), and CS205 (Computing Foundations). Completion of a course in reproducible research is also required. Many of these will also contribute towards the required advanced doctoral courses in Biostatistics. To help trainees develop leadership and communication skills, HPM 223 (Public Speaking), HPM 252 (Negotiation), and HPM 539 (Health Care Organizations) are highly recommended.
Summer Project: Trainees must complete a summer project after their first year in the PhD program that has clear ties to Big Data. The summer project will be considered as the second of the three required lab rotations, and the summer project presentation will substitute for the required 2-page lab report. The Training Grant Director and other training grant mentors may help students identify appropriate summer projects.
Seminars/Working Groups: Trainees should attend the Big Data Seminar and may be asked to help organize the seminar series. Trainees should attend the B3D (Biostatistics-Biomedical Informatics Big Data) Seminar or the Program in Quantitative Genomics (PQG) monthly seminars and working groups if interested in genomics.
Annual Conference: Trainees are encouraged to attend one professional conference each year, such as the ENAR meeting of the International Biometric Society, JSM, Machine Learning meeting or other appropriate conference.
Lab Rotations: Trainees will be expected to complete three lab rotations involving Big Data (BD) in the first two years: BD biostatistics, BD computing, and a BD health science area, with the inclusion of a wet lab rotation if interested in genomics. Trainees will be required to complete each rotation by registering for BST314 and completing the rotation form before starting a rotation, and then preparing a 2-page report summarizing his/her lab rotation activities. The lab director will be required to complete an evaluation report of the trainee on the evaluation form after the rotation is completed. The Training Grant director can help identify appropriate rotations if needed, but potential lab rotation mentors may also be found at the Big Data Training Grant link noted below.
Dissertation: Trainees should have dissertation topics which are related to biostatistical methodology in Big Data. Proposals for incorporating Big Data and/or computational applications and reproducible research in student dissertation work should be included as part of the oral qualifying exam. Even if students are no longer funded on this grant during the period of their dissertation research, it is expected that support over the previous years in the PhD program will be acknowledged by focusing on a Big Data applications in biostatistics or bioinformatics.
Progress Report: Trainees will be asked to provide an annual progress report including details on how their training relates to Big Data Health Sciences, to be included in annual NIH progress reports for the training grant.