The ever-increasing accumulation of data continues to outstrip the graduate training needed to meaningfully mine the data collected. This issue is further complicated by the fact that holistic training in biomedical big data analysis requires PhD level expertise in not one, but three core research areas: (1) biology (2) statistics and (3) computer science, yet the majority of traditional PhD training programs demand that students choose just one of these areas as their focus. A growing number of biomedical PhD students are recognizing the need to develop data analysis and computational biology skills, at the same time that a growing number of computer science and statistics PhD students are realizing that their marketability could be substantially expanded if they knew how to apply their skills to solve outstanding problems in the health arena. The purpose of this pre-doctoral training program we are proposing to introduce at The University of Texas at Austin is for the trainee to become an expert in one of the following areas: 1. Statistics (STAT); 2. Computer Science (CS); 3. Computational science, engineering, and mathematics (CSEM); or 4. Biology (via a PhD in one of a. neuroscience [NS]; b. ecology, evolution, and behavior [EEB]; c. cell and molecular biology [CMB]; or d. Biomedical Engineering [BME]) while also obtaining essential training in all three core areas (statistics, computer science, and biology). This will ideally equip the graduates from this program to make important scientist c discoveries using big data. The challenge is in developing a program that trains these multidisciplinary skills without sacrificing strength in ther core PhD area. This is an exciting opportunity for the new PhD program in statistics and the already established PhD programs involved, and it is consistent with the interdisciplinary emphasis of all the faculty involved with this application. This training program will differ from he standard training programs at UT- Austin by incorporating new courses, a new seminar/workshop, and program-specific rotations during year 3. These rotations will provide opportunities for trainees to work in research labs in the new University of Texas at Austin Dell Medical School and the Dell Pediatric Research Institute. Research at the interface of these three areas requires excellent collaborative skills. In addition to subject matter training, we wil help trainees develop strong oral and written communication skills. This combination of knowledge and communication will equip the trainees to make major contributions to big data biomedical science. We anticipate funding five trainees per year. Trainees will formally start the training program during year 2 of their PhD programs.

Public Health Relevance

This innovative graduate program will help to train a new generation of scientists with expertise in statistics, computer science, and biology in order to solve important public health problems involving big data and improve overall health.

National Institute of Health (NIH)
National Library of Medicine (NLM)
Institutional National Research Service Award (T32)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Ye, Jane
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Texas Austin
Schools of Arts and Sciences
United States
Zip Code