Stanford BSSR Pre-Doctoral Training Program at the Intersection of Data Sciences with Behavioral, Social, and Population Health Research

Stanford University, Stanford CA

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Abstract

PROJECT SUMMARY / ABSTRACT The Stanford Department of Epidemiology and Population Health requests five more years of support for its Behavioral and Social Science Research Pre-Doctoral Training program at the intersection of data science and population health research. The objective of the program is to support and train a community of scholars with the skills and knowledge necessary to apply advanced quantitative skills to novel and complex datasets to better understand and prevent heart, lung, blood, and sleep disorders. Our first four cohorts have been successful and productive with the cohort that has graduated all finding related positions in academia or private sector research. The request for continued support builds on those successes and expands the current project with more focused training the analysis of real-world data for social science research. We have refined the mentorship team but retain the diverse set of research backgrounds from across the social and quantitative sciences at Stanford University. We will continue to provide a transformative multi-disciplinary predoctoral training environment that draws mentors from social science disciplines (economics, psychology, sociology) and quantitative disciplines (computer science, informatics, statistics). The graduates of our program will have rigorous training in their own scientific disciplines, combined with extensive expertise working on a broad range of innovative research projects that rely on data of primarily two types: (1) intensive or voluminous longitudinal data from mHealth, smartphone and sensor technologies or electronic health records, and/or (2) large and complex data from internet, commercial, health administrative records, large population databases, internet data and social media platforms, crowd sourcing, and citizen science data. We are requesting support for 5 pre-doctoral students per year whose training will last 2 or more years. They will emerge from the program with a thorough understanding of their own fundamental discipline combined with advanced expertise in cutting- edge statistical and computational methods for analyzing increasingly complex and multidimensional longitudinal sets. The training program components will include both department or discipline-specific training in addition to program-wide data science components including: (1) innovative curriculum, including specialized quantitative curriculum customized to the experience and background of each trainee; (2) a mentored research experience with a dual mentor model (one disciplinary mentor, one methodological mentor); (3) exposure to team science approaches to problem solving, including design thinking, cross-disciplinary collaborations, and team building; (4) experiential components, including availability of Stanford dry-lab rotations and short-term internships in Silicon Valley companies; (5) forums for intellectual exchanges; and (6) many opportunities to develop professional skills in grant writing and collaboration. The graduates from this training program will have the capability to conduct cutting edge social science based research applied to the prevention and treatment of heart, lung, blood, and sleep disorders and improve outcomes among patients with these disorders.