Transforming Analytical Learning in the Era of Big Data

项目摘要 在这个“大数据”的黎明时代，招募和培训下一代生物医学数据科学家至关重要， 大数据。生物医学科学中的“大数据”收集正在迅速增长，并有可能解决 当今许多迫切的医疗需求，包括个性化医疗、根除疾病和治愈 癌实现大数据的好处将需要新一代（生物）统计和 计算方法，他们将能够开发必要的方法和工具来解锁信息 包含在大型异构数据集中。非常需要在这方面受过专门训练的科学家， 异构、跨学科的健康大数据新领域。因此，招聘人才 科学，技术，工程和数学（STEM）课程的本科生对我们的能力至关重要， 利用“大数据”提供的潜力和它提出的挑战。 密歇根大学本科生暑期学院：转变分析学习的时代 大数据将主要利用来自三个不同部门的教师的专业知识和经验， 密歇根大学的三个不同的学院：公共卫生学院的生物统计学，计算机 工程学院的科学，文学，科学和艺术学院的统计。学院 教师和导师具有统计学，计算机科学，信息科学，医学， 人口健康、社会和生物科学。他们在广泛的领域有积极的研究计划， 方法学领域包括统计建模、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 维度统计，分布式计算，缺失数据，因果推理，数据管理和集成， 信号处理和医学成像。他们研究的疾病和状况包括肥胖、糖尿病、 心血管疾病、癌症、神经系统疾病、肾脏疾病、损伤、黄斑变性和 老年痴呆症生物学的领域包括神经科学、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学员将具有较强的定量技能和 背景在STEM暑期学院将包括课程的组合，以提高技能， 参与者的兴趣到足够的水平，考虑在“大数据”科学研究生学习，沿着 有一个深入的指导组件，将允许参与者研究一个特定的主题/项目，利用 大数据。我们已经目睹了巨大的热情和成功与目前的夏季计划大 数据由该团队领导，在过去4年（2015-2018年）培训了164名学生，其中包括90名女学生 和30名来自少数民族的学生。其中14名参与者来自过去三年 年目前是密歇根生物统计学的研究生。正在进行的计划在全球范围内获得了关注 全国暑期研究项目的录取率为20%，录取率为80% 在那些被提供这个机会的人中。该计划一直受到非常强烈的评价 我们过去的校友已经成为我们项目的品牌大使和倡导者。我们计划在 该计划在该赠款的下一个三年资助周期（2019-2021）中的成功和遗产。 我们在大数据暑期学院的首要目标是招募和培养下一代大数据 数据科学家使用非传统的、基于行动的学习范式。这个长达六周的夏天 学院将在国内和国际上招收大约45名本科生，其中20名来自国内 由所要求的SIBS资助机制支持的学生和其他由补充资助的学生 机构和基金会支助。我们建议让学员接触各种技术、技能和 健康大数据领域的问题。他们将由一个跨学科的教师团队授课和指导， 反映了大数据研究所需的共享知识景观。他们将参与指导研究 健康大数据的三个主要领域的项目：电子健康记录/医疗索赔，基因组学和 显像其中一些项目将在心血管精准医学领域进行定义， 一个高度量化的研究人员团队，从事使用大数据的心血管研究。结束时 该计划将有一个总结顶点研讨会展示学生的研究，通过 海报和口头介绍。届时将有密歇根大学研究人员，外部客人和专业人士的讲座 发展讲习班，为学生进入研究生院做准备。我们建议SIBS之间的合作 与杜尔特学院暑期项目的学员谁将参加这次总结研讨会。的资源 为暑期研究所开发的，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，以便这种格式可以在世界任何地方复制。这 民主传播计划将导致本科生获得教学和培训材料 in this new新field领域across横过the world世界.我们将提供多种专业发展机会和资源 为我们的学员提供研究生院准备，以便他们能够在大四之后进行反思和计划。所有 我们建议的活动体现在我们的三个具体目标：教学、指导和传播。

PROJECT SUMMARY 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 of health big data. 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' offers and the challenges that it presents. The University of Michigan Undergraduate Summer Institute: Transforming Analytical Learning in the Era of Big Data will primarily draw from the expertise and experience of faculty from three different departments within three 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. The faculty instructors and mentors have backgrounds in Statistics, Computer Science, Information Science, Medicine, Population Health, Social and Biological Sciences. They have active research programs in a broad spectrum of methodological areas including statistical modeling, 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 medical imaging. The diseases and conditions they study include obesity, diabetes, cardiovascular disease, cancer, 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 success with the current summer program on Big Data led by this team with 164 students trained in the last 4 years (2015-2018) including 90 female students and 30 students from underrepresented minority groups. Fourteen of these participants from the last three years are currently graduate students in Michigan Biostatistics. The ongoing program has gained traction in the national landscape of summer research programs with 20% rate of admission and 80% rate of acceptance among those who are offered this opportunity. The program has consistently received very strong evaluation and our past alumni have become brand ambassadors and advocates for our program. We plan to build on the success and legacy of this program in the next three year funding cycle of this grant (2019-2021). The overarching goal of our summer institute in big data is to recruit and train the next generation of big data scientists using a non-traditional, action-based learning paradigm. This six week long summer institute will recruit a group of approximately 45 undergraduates nationally and internationally, with 20 domestic students supported by the requested SIBS funding mechanism and others supported by supplementary institutional and foundation support. We propose to expose the trainees to diverse techniques, skills and problems in the field of health Big Data. They will be taught and mentored by a team of interdisciplinary faculty, reflecting the shared intellectual landscape needed for Big Data research. They will engage in mentored research projects in three primary areas of health big data: Electronic Health Records/Medical Claims, Genomics and Imaging. Some of the projects will be defined in the area of cardiovascular precision medicine, defined by a team of highly quantitative researchers engaged in cardiovascular research that uses big data. 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 U-M researchers, outside guests and a professional development workshop to prepare the students for graduate school. We propose an inter-SIBS collaboration with Dordt College summer program trainees who will attend this concluding symposium. 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. We will offer multiple professional development opportunities and resources for graduate school preparation to our trainees so that they can reflect and plan beyond their senior year. All of our proposed activities are reflected through our three specific aims: Teaching, Mentoring and Dissemination.