Summer Institute for Statistics of Big Data

大数据统计暑期学院

• 批准号: 8935790
• 负责人: ALI SHOJAIE
• 金额: $ 15.96万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-29 至 2017-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8935790
• 关键词: Academia; Area; Big Data; Biology; Biomedical Computing; Biomedical Research; Biometry; Cancer Center; Case Study; Collection; Computer software; Computerized Medical Record; DNA Sequence; Data; Data Set; Diabetes Mellitus; Educational process of instructing; Educational workshop; Ensure; Environment; Exposure to; Faculty; Fred Hutchinson Cancer Research Center; Funding; Government; Health; Human; Hybrids; Imagery; Individual; Industry; Institutes; Iowa; Knowledge; Learning; Learning Module; Machine Learning; Malignant Neoplasms; NCI Center for Cancer Research; North Carolina; Participant; Persons; Postdoctoral Fellow; Process; Records; Research; Research Personnel; Resources; Rice; Risk; Running; Scholarship; Science; Slide; Statistical Computing; Statistical Methods; Students; Training; Training Activity; Training Programs; Travel; United States; Universities; Videotape; Wages; Washington; Work; base; biomedical scientist; data visualization; graduate student; human disease; improved; instructor; lectures; member; open source; programs; skills; statistics; teacher; web site

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.

描述：华盛顿大学大数据统计夏季研究所 (SISBID) 正在寻求资金。该计划将提供有关访问、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。该项目将由华盛顿大学生物统计学系的 Ali Shojaie 和 Daniela Witten 共同指导。 SISBID 项目将包括每年 7 月在华盛顿大学教授的五门为期 2.5 天的面对面课程或模块。个人参与者可以注册他或她选择的任何一组模块。五个模块如下：（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)生物医学大数据的可重复性研究。每个模块将由正式讲座和动手计算实验室相结合。参与者将以团队形式合作，将他们在每个模块中培养的技能应用于从相关案例研究中得出的重要问题。 SISBID 的主要受众将包括希望开发利用生物医学大数据所需的统计和计算培训的生物医学科学家。次要受众将由具有较强统计或计算背景但很少接触生物学的个人组成，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。参与者将包括来自工业界、政府和学术界的高年级本科生、研究生、博士后和研究人员。为了确保所有参与者都能充分参与该计划，参与者需要具备一定的 R 编程和统计推断背景，这可以通过在计划开始前参加两门免费在线课程来获得。 五个模块中的每个模块将由两名讲师共同教授。这10位导师将来自华盛顿大学、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、北卡罗来纳大学等美国顶尖大学和研究中心。他们的选择是基于研究专业知识和卓越的教学。 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。 该提案特别要求为每年 55 名学生/博士后研究员旅行奖学金、每年 130 名学生/博士后研究员注册奖学金提供资金， 讲师差旅费和津贴、助教津贴和 PI 工资支持。