UW Awarded NIH Grant for Training in Advanced Data Analytics for Behavioral and Social Sciences
The University of Washington’s Center for Studies in Demography & Ecology (CSDE) along with partners in the Center for Statistics in the Social Sciences (CSSS) and the eSciences Institute are one of eight awardees across the country selected to develop training programs in advanced data analytics for advancing population health through the National Institutes of Health’s (NIH’s) Office of Behavioral and Social Sciences (OBSSR). At the University of Washington, this 5-year, $1.8 million training program will fund 25 academic year graduate fellowships, develop a new training curriculum and contribute to methodological advances in health research at the intersection of demography and data science.
Professors Tyler McCormick (Sociology and Statistics) and Jon Wakefield (Biostatistics and Statistics) led the initiative with support from Sara Curran (International Studies, Sociology & Public Policy), along with faculty affiliated with CSDE, CSSS, and the eSciences Institute. The program will build on CSDE’s successful graduate certificate in demographic methods by integrating training in advanced statistics and computational methods. The T32 Training Program PI is newly appointed Assistant Professor of Sociology and Senior Data Scientist Dr. Zack Almquist.
The inaugural cohort will begin the program in October 2020, and is composed of Ian Kennedy (Graduate student in Sociology), Neal Marquez (Graduate student in Sociology), Emily Pollock (Graduate student in Anthropology), Aja Sutton (Graduate student in Geography), and Crystal Yu (Graduate student in Sociology). Kennedy’s research is centered on developing and applying methods of automated data collection and text analysis to better understand how race and class intersect with property advertisements on platforms such as Craigslist; Marquez’s research uses novel data made available by SafeGraph to understand mobility patterns in the United States; Pollock’s research employs novel computational methods and social network analysis to model disease transmission; Sutton’s research applies computational methods to understand how geography impacts disease spread; and Yu’s research looks to improve small area estimation techniques through the intersection of administrative data, statistics and trace data. This first cohort of trainees reflects the UW’s strengths in data science across a wide array of types of data and analytic approaches.
The other universities awarded similar grants include Emory, Johns Hopkins University, Stanford, University of Arkansas Medical Center, UC-Berkeley, UC-San Diego, and UC-San Francisco. Faculty and trainees participating in the eight programs will collaborate with OBSSR to advance methodological innovation and enhancing training through Predoctoral Training in Advanced Data Analytics for Behavioral and Social Sciences Research (BSSR) – Institutional Research Training Program (RFA-OD-19-011). This funding opportunity was designed to fill educational gaps and needs in the behavioral and social sciences research community that are not being addressed by existing educational opportunities. More information about the national initiative can be found on the OBSSR website training page.
The NIH review offered high praise for UW’s training program. “The leadership team has well established credentials, complementary expertise, and a strong track record and the proposed program builds on an existing program with demonstrable record of success,” noted reviewers. “The curriculum – which offers coursework in statistical methods, machine learning, coding, databases, data visualization, and data ethics – is well-thought out and will provide trainees with numerous immersive opportunities. The data science training steering committee is excellent and complements traditional training models.”
Additionally, reviewers noted that a “major strength of the curriculum is that the proposed training is not specific to any one data structure but is designed to provide the methodological and theoretical tools to analyze any data using foundational principles and modern computational techniques.”