Theory and experimentation historically have been the two dominant paradigms for scientific discovery, with large-scale computer simulations occurring as the third paradigm in the 20th century. The paradigm for scientific discovery, dubbed "Data Science", is currently emerging as a result of the availability of exponentially growing volumes of data from a variety of sources e.g., people (e.g., online social networks, mobile devices and Internet of Things), businesses (e.g., business analytic tools), and large scientific instruments (e.g., high-throughput DNA sequencing devices, telescopes and colliders) popularly known as “Big Data.” In this paradigm, scientists seeks to exploit the information buried in complex and massive data sets to drive scientific discovery. Data Science is an essential complement to the three existing paradigms, with a widespread and growing set of applications in almost all disciplines (so-called, data science application disciplines), including health informatics, geosciences, precision and personalized medicine, business analytics, smart cities and cybersecurity, to name a few. While borrowing basic principles from computer science, mathematics, and other STEM disciplines (so-called, data science core disciplines), Data Science is evolving from a multidisciplinary practice into a new, interdisciplinary field of science with a set of independent principles that are expected to evolve only through joint studies between core (STEM) and application (non-STEM) disciplines; a true example of “CS+X.”
Currently, a huge gap exists between the demand from industry and academia for data scientists, and the current number of qualified individuals in the workforce. While a number of Data Science principled education programs have recently emerged in response, they tend to focus on either core disciplines or application disciplines, training scientists originally educated in one league to learn about research methodologies in the other as an afterthought remedy. We believe best data scientists are those that are simultaneously trained on methodologies from both core and application disciplines, having the chance to learn core principles of the data science in the context of various data science disciplines.
Toward that end, sponsored by the U.S. Department of Education (DoEd), this GAANN (Graduate Assistance in Areas of National Need) Program, titled Big Data Science and Engineering GAANN Program (BDSE GAANN Program, or BDSE Program for short), is established as an integrated interdisciplinary education and research program that trains students in all Big Data Science and Engineering core disciplines with sufficient exposure to various application domains to become data scientists in essence. BDSE graduates will be equipped with all core skills and tools they need to tackle data science problems in a variety of application domains. This is accomplished by engaging faculty with complementary expertise from both core disciplines (namely, Computer Science, and Mathematical and Statistical Sciences) and application disciplines (e.g., Medicine and Public Health, Business, Geosciences). Correspondingly, during the BDSE program each fellow will be advised by a team of at least two advisors, one from core disciplines, and one or more from application disciplines in which the fellow has special interest.
The fellowship program will provide financial support for fellows in the form of tuition & fees, in addition to up to $34,000 annual stipend for up to three years. Note that the amount of financial support may be adjusted so as not to exceed the fellow’s level of financial need as determined under title IV, part F, of the Higher Education Act for the period of the fellow's enrollment.
