Summer Institute for Statistics of Big Data

描述：为华盛顿大学的夏季大数据统计研究所(SISBID)寻求资金。该计划将提供访问、处理、管理和分析大型生物医学数据集所需的统计和计算技能的研讨会。它将由华盛顿大学生物统计学系的Ali Shojaie和Daniela Witten共同执导。SISBID项目将包括五个为期2.5天的面对面课程，每年7月在华盛顿大学授课。个人参与者可以注册他或她选择的任何一组模块。这五个模块是：(1)访问生物医学大数据；(2)数据可视化；(3)有监督的统计机器学习方法；(4)无监督的统计机器学习方法；(5)生物医学大数据的可重复性研究。每个单元将由正式讲座和实际操作的计算实验室组成。参与者将以小组形式合作，以便将他们在每个模块中发展的技能应用于从相关案例研究中提取的重要问题。SISBID的主要受众将包括生物医学科学家，他们希望开发利用生物医学大数据所需的统计和计算培训。次要受众将包括具有更强的统计或计算背景但对生物学几乎没有接触的个人，他们将学习如何将自己的技能应用于与生物医学大数据相关的问题。参与者将包括高级本科生、研究生、博士后研究员和研究人员，并将来自行业、政府和学术界。为了确保所有参与者能够充分参与该计划，预计参与者将在R方案编制和统计推断方面已经有一些先前的背景，这些背景可以在该计划开始之前通过参加两个免费的在线课程来获得。五个单元中的每个单元都将由两名教师共同讲授。这10名教师将来自美国顶尖大学和研究中心，如华盛顿大学、莱斯大学、爱荷华大学、约翰霍普金斯大学、MD安德森癌症研究中心、弗雷德·哈钦森癌症研究中心和北卡罗来纳大学。他们是根据研究专长和教学方面的卓越表现挑选出来的。讲座视频和幻灯片将在网上免费提供，以便无法亲自参加SISBID的个人仍然可以从该计划中受益。该提案特别要求每年提供55个学生/博士后旅行奖学金，每年130个学生/博士后注册奖学金， 讲师旅费和津贴、助教津贴和PI工资支持。

DESCRIPTION: Funding is sought for the Summer Institute for Statistics of Big Data (SISBID) at the University of Washington. This program will provide workshops on the statistical and computational skills needed to access, process, manage, and analyze large biomedical data sets. It will be co-directed by Ali Shojaie and Daniela Witten, faculty in the Department of Biostatistics at University of Washington. The SISBID program will consist of five 2.5-day in-person courses, or modules, taught at the University of Washington each July. An individual participant can register for whichever set of modules he or she chooses. The five modules are as follows: (1) Accessing Biomedical Big Data; (2) Data Visualization; (3) Supervised Methods for Statistical Machine Learning; (4) Unsupervised Methods for Statistical Machine Learning; (5) Reproducible Research for Biomedical Big Data. Each module will consist of a combination of formal lectures and hands-on computing labs. Participants will work together in teams in order to apply the skills that they develop in each module to important problems drawn from relevant case studies. The primary audience for SISBID will consist of biomedical scientists who would like to develop the statistical and computational training needed to make use of Biomedical Big Data. The secondary audience will consist of individuals with stronger statistical or computational backgrounds but little exposure to biology, who will learn how to apply their skills to problems associated with Biomedical Big Data. Participants will include advanced undergraduates, graduate students, post-doctoral fellows, and researchers, and will be drawn from industry, government, and academia. In order to ensure that all participants are able to fully engage in the program, participants will be expected to already have some prior background in R programming and statistical inference, which can be obtained by taking two free online courses before the program begins. Each of the five modules will be co-taught by two instructors. The ten instructors will be drawn from top universities and research centers across the U.S., such as the University of Washington, Rice University, University of Iowa, Johns Hopkins University, MD Anderson Cancer Research Center, Fred Hutchinson Cancer Research Center, and University of North Carolina. They have been selected based on research expertise and excellence in teaching. Lecture videos and slides will be made freely available online so that individuals who are unable to attend SISBID in person can still benefit from the program. This proposal specifically requests funds for 55 student / postdoctoral fellow travel scholarships per year, 130 student / postdoctoral fellow registration scholarships per year, instructor travel and stipends, teaching assistant stipends, and PI salary support.