Summer Institute for Statistics of Big Data

描述：华盛顿大学大数据统计暑期研究所（SISBID）正在寻求资金。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讲习班。它将由华盛顿大学生物统计学系的教员Ali Shojaie和Daniela Witten共同领导。SISBID项目将包括五个2.5天的面对面课程，或模块，每年7月在华盛顿大学教授。个人参与者可以注册他或她选择的任何一组模块。这五个模块分别是：(1)获取生物医学大数据；(2)数据可视化；(3)统计机器学习的监督方法；(4)统计机器学习的无监督方法；(5)生物医学大数据可重复性研究。每个模块将包括正式的讲座和动手计算实验。参与者将在团队中合作，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得出的重要问题。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较强统计或计算背景但很少接触生物学的个人，他们将学习如何将他们的技能应用于与生物医学大数据相关的问题。参与者将包括来自工业界、政府和学术界的高级本科生、研究生、博士后研究员和研究人员。为了确保所有参与者都能充分参与到项目中来，参与者应该已经有一些R编程和统计推断的背景，这些可以通过在项目开始之前参加两个免费的在线课程来获得。五个模块中的每个模块将由两名教师共同教授。这10名讲师将从华盛顿大学、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、北卡罗莱纳大学等美国顶尖大学和研究中心选出。他们是根据研究专长和卓越的教学水平选出的。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55名学生/博士后旅行奖学金，每年130名学生/博士后注册奖学金，

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.