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：华盛顿大学的夏季大数据统计研究所（Sisbid）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。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计划将由每年7月在华盛顿大学教授的五个2.5天的面对面课程或模块组成。单个参与者可以为他或她选择的一套模块注册。这五个模块如下：（1）访问生物医学大数据； （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）可再现的生物医学大数据研究。每个模块将包括正式的讲座和动手计算实验室的组合。参与者将共同努力，以便将他们在每个模块中开发的技能应用于相关案例研究中得出的重要问题。 Sisbid的主要受众将由生物医学科学家组成，他们想开发利用生物医学大数据所需的统计和计算培训。二级受众将包括具有更强统计或计算背景的人，而对生物学的接触很少，他们将学习如何将其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，并将从行业，政府和学术界汲取灵感。为了确保所有参与者都能够完全参与该计划，将期望参与者在R编程和统计推断中已经具有一些先前的背景，可以通过在计划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位讲师共同任教。这10位讲师将来自美国的顶尖大学和研究中心，例如华盛顿大学，赖斯大学，爱荷华大学，约翰·霍普金斯大学，医学博士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。它们是根据研究专业知识和卓越教学的选择。 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。 该建议专门要求每年为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， 讲师旅行和津贴，助教津贴和PI薪水支持。