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

项目概要/摘要 斯坦福大学 BSSR 博士前培训项目的使命是数据科学与 行为、社会和人口健康研究旨在开发跨校园协作培训 计划为有才华的博士前学生提供交叉领域的高级专业培训 社会和行为健康科学、社会流行病学、数据科学和人口健康。我们是 建立一支横跨斯坦福大学校园的新团队，其中包括来自 6 个学院和 11 个系的教师。我们 将提供一个变革性的多学科博士前培训环境，吸引来自 不同领域（健康与社会心理学、医学社会学、社会流行病学、传播学、健康 经济学、商业、教育、法律）和定量学科（计算机科学、信息学、统计学）。 我们项目的毕业生将在自己的科学学科中接受严格的培训，并结合 广泛的专业知识致力于广泛的创新研究项目，这些项目主要依赖于两个方面的数据 类型：(1) 来自移动医疗、智能手机和传感器技术的密集或大量纵向数据或 电子健康记录，和/或 (2) 来自互联网、商业、卫生行政的大量复杂数据 记录、大量人口数据库、互联网数据和社交媒体平台、众包和公民 科学数据。研究生第一年或第二年的博士前培训生将从 健康心理学、医学社会学、社会/行为流行病学、健康经济学或 另一个社会科学学科。我们每年请求支持 5 名博士前学生，他们的 培训将持续2年或更长时间。他们将从该计划中彻底了解自己的情况 自己的基础学科与尖端统计和计算方面的先进专业知识相结合 分析日益复杂和多维纵向集的方法。培训计划 除了整个计划的数据之外，各个组成部分还将包括部门或学科特定的培训 科学组成部分包括：（1）创新课程，包括专门的定量课程 根据每位学员的经验和背景进行定制； (2) 具有双重指导的研究经验 导师模式（一名学科导师，一名方法论导师）； (3) 接触团队科学方法 解决问题，包括设计思维、跨学科合作和团队建设； (4) 体验部分，包括斯坦福干实验室轮换和硅短期实习 山谷公司； （五）知识交流论坛； (6) 大量的职业发展机会 拨款写作和协作的技能。 BSSR 毕业生将有能力开展前沿工作 对行为、社会和健康问题以及预防和治疗干预措施的研究 有潜力降低心、肺、血液和睡眠 (HLBS) 疾病的风险并改善结果 患有这些疾病的患者中。

Project Summary / Abstract The mission of the Stanford BSSR Pre-Doctoral Training Program at the Intersection of Data Sciences with Behavioral, Social, and Population Health Research is to develop a cross-campus collaborative training program to provide talented pre-doctoral students with advanced specialized training at the intersections of social and behavioral health science, social epidemiology, data science, and population health. We are building a new team that spans the Stanford campus including faculty from 6 schools and 11 departments. We will provide a transformative multi-disciplinary predoctoral training environment that draws mentors from diverse fields (health and social psychology, medical sociology, social epidemiology, communications, health economics, business, education, law) 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. Predoctoral trainees in their first or second year of graduate studies will be admitted from programs in health psychology, medical sociology, social/behavioral epidemiology, health economics or another social science discipline. 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. BSSR graduates will have the capability to conduct cutting edge research on behavioral and social, and health issues, as well as prevention and treatment interventions that have the potential to reduce the risks of heart, lung, blood, and sleep (HLBS) disorders and improve outcomes among patients with these disorders.