Administrative Supplement Request for Transforming Analytical Learning in the Era of Big Data

大数据时代变革分析学习的行政补充请求

• 批准号: 9243811
• 负责人: Timothy D Johnson
• 金额: $ 15.94万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-30 至 2018-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9243811
• 关键词: Administrative Supplement; Adverse drug effect; Alzheimer' s Disease; Area; Arts; Behavioral Sciences; Big Data; Biological Markers; Biological Sciences; Biology; Biometry; Cardiovascular Diseases; Case Study; Code; Collection; Computing Methodologies; Data; Data Science; Data Set; Development; Diabetes Mellitus; Disease; Educational process of instructing; Educational workshop; Engineering; Epigenetic Process; Faculty; Funding; Generations; Genetic; Genomics; Goals; Grant; Health; Image; Imagery; Information Sciences; Injury; Kidney Diseases; Lead; Learning; Literature; Machine Learning; Macular degeneration; Malignant Neoplasms; Medical; Mentors; Methodology; Methods; Michigan; Natural Language Processing; Neurosciences; Obesity; Oral; Participant; Positioning Attribute; Prevention; Problem Sets; Public Health Informatics; Public Health Schools; Recruitment Activity; Research; Research Personnel; Resources; Schools; Science; Science, Technology, Engineering and Mathematics; Scientist; Statistical Methods; Students; Talents; Techniques; Training; Universities; Work; base; burden of illness; cluster computing; college; computer science; data integration; data management; data mining; design; experience; instructor; interest; lectures; meetings; member; metabolomics; nervous system disorder; next generation; novel therapeutics; open source; personalized medicine; posters; programs; response; signal processing; skills; statistics; success; summer institute; symposium; tool; undergraduate student; wiki

 DESCRIPTION (provided by applicant): In this dawning era of `Big Data' it is vital to recruit and train the next generation of biomedical data scientists in `Big Data'. The collection of `Big Data' in the biomedical sciences is growing rapidly and has the potential to solve many of today's pressing medical needs including personalized medicine, eradication of disease, and curing cancer. Realizing the benefits of Big Data will require a new generation of leaders in (bio) statistical and computational methods who will be able to develop the approaches and tools necessary to unlock the information contained in large heterogeneous datasets. There is a great need for scientists trained in this specialized, highly heterogeneous, and interdisciplinary new field. Thus, the recruitment of talented undergraduates in science, technology, engineering and mathematics (STEM) programs is vital to our ability to tap into the potential that `Big Data' offer and the challenges that it presents. The University of Michigan Undergraduate Summer Institute: Transforming Analytical Learning in the Era of Big Data will draw from the expertise and experience of faculty from four different departments within four different schools at the University of Michigan: Biostatistics in the School of Public Health, Computer Science in the School of Engineering, Statistics in the College of Literature, Sciences and the Arts, and Information Science in the School of Information. The faculty instructors and mentors have backgrounds in Statistics, Computer Science, Information Science and Biological Sciences. They have active research programs in a broad spectrum of methodological areas including data mining, natural language processing, statistical and machine learning, large-scale optimization, matrix computation, medical computing, health informatics, high-dimensional statistics, distributed computing, missing data, causal inference, data management and integration, signal processing and imaging. The diseases and conditions they study include obesity, cancer, diabetes, cardiovascular disease, neurological disease, kidney disease, injury, macular degeneration and Alzheimer's disease. The areas of biology include neuroscience, genetics, genomics, metabolomics, epigenetics and socio-behavioral science. Undergraduate trainees selected will have strong quantitative skills and a background in STEM. The summer institute will consist of a combination of coursework, to raise the skills and interests of the participants to a sufficient level to consider pursuing graduate studies in `Big Data' science, along with an in depth mentoring component that will allow the participants to research a specific topic/project utilizing `Big Data'. We have witnessed tremendous enthusiasm and response for our pilot offering in 2015 with 153 applications for 20 positions and a yield rate of 80% from the offers we extended. We plan to build on the success of this initial offering in the next three year funding cycle of this grant (2016-2018). The overarching goal of our summer institute in big data is to recruit and train the next generation of big data scientists using a no-traditional, action-based learning paradigm. This six week long summer institute will recruit a group of approximately 30 undergraduates nationally and expose them to diverse techniques, skills and problems in the field of Big Data. They will be taught and mentored by a team of interdisciplinary faculty, reflecting the shared intellectual landscape needed for Big Data research. At the conclusion of the program there will be a concluding capstone symposium showcasing the research of the students via poster and oral presentation. There will be lectures by UM researchers, outside guests and a professional development workshop to prepare the students for graduate school. The resources developed for the summer institute, including lectures, assignments, projects, template codes and datasets will be freely available through a wiki page so that this format can be replicated anywhere in the world. This democratic dissemination plan will lead to access of teaching and training material for undergraduate students in this new field across the world.

 描述(由申请者提供)：在这个“大数据”的曙光时代，招募和培训“大数据”方面的下一代生物医学数据科学家至关重要。生物医学科学中的“大数据”收集正在迅速增长，有可能解决当今许多紧迫的医疗需求，包括个性化医疗、根除疾病和治愈癌症。实现大数据的好处将需要新一代(BIO)领导者 统计和计算方法将能够开发必要的方法和工具，以解锁大型异质数据集中包含的信息。在这一专门的、高度不同的、跨学科的新领域，非常需要训练有素的科学家。因此，招收科学、技术、工程和数学(STEM)专业的有才华的本科生，对于我们挖掘‘大数据’提供的潜力和它带来的挑战的能力至关重要。密歇根大学本科生暑期学院：在大数据时代转变分析学习将借鉴密歇根大学四个不同学院四个不同系的教师的专业知识和经验：公共卫生学院的生物统计学，工程学院的计算机科学，文学、科学和艺术学院的统计学，以及信息学院的信息科学。这些教师和导师具有统计学、计算机科学、信息科学和生物科学的背景。他们在广泛的方法学领域拥有活跃的研究项目，包括数据挖掘、自然语言处理、统计和机器学习、大规模优化、矩阵计算、医疗计算、健康信息学、高维统计、分布式计算、缺失数据、因果推理、数据管理和集成、信号处理和成像。他们研究的疾病和状况包括肥胖、癌症、糖尿病、心血管疾病、神经疾病、肾脏疾病、损伤、黄斑变性和阿尔茨海默病。生物学领域包括神经科学、遗传学、基因组学、代谢组学、表观遗传学和社会行为科学。被选中的本科生将具有很强的量化技能和STEM背景。暑期研习班将包括课程作业，以将参与者的技能和兴趣提高到足以考虑攻读“大数据”科学研究生课程的水平，以及使参与者能够利用“大数据”研究特定主题/项目的深入指导部分。我们见证了2015年试点招聘会的巨大热情和反响，20个职位的153份申请，以及我们延长的招聘期的收益率为80%。我们计划在这笔赠款的下一个三年资金周期(2016-2018年)中，在首次公开募股的成功基础上再接再厉。我们夏季大数据学院的总体目标是招募和培训使用非传统、基于行动的学习范式的下一代大数据科学家。这个为期六周的暑期学院将在全国范围内招募约30名本科生，让他们接触到大数据领域的各种技术、技能和问题。他们将由一个跨学科教员团队授课和指导，反映出大数据研究所需的共享智力格局。在项目结束时，将举行一个总结的顶石研讨会，通过海报和口头演示展示学生的研究成果。将有密歇根大学研究人员的讲座、外部嘉宾和专业发展研讨会，为学生进入研究生院做准备。为暑期研习所开发的资源，包括讲座、作业、项目、模板代码和数据集，将通过维基页面免费提供，以便这一格式可在世界任何地方复制。这一民主传播计划将为世界各地的本科生提供这一新领域的教学和培训材料。