REU Site: Computational Methods for Discovery Driven by Big Data

REU 网站：大数据驱动的发现计算方法

• 批准号: 1460620
• 负责人: Daniel Boley
• 金额: $ 36万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-05-01 至 2019-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1460620&HistoricalAwards=false
• 关键词: REU; Site; Computational; Methods; Discovery

As participants in the University of Minnesota (UMN) REU Site program, students will engage in research that develops computational methods for scientific discovery across disciplines that are driven by big data. In this 10-week summer program, in addition to immersion in research, students will receive technical training and professional development that encourages and prepares them for a sustained career in the sciences. This includes Big Data Colloquia, Communicating Science workshops, career mentoring, and public dissemination of research findings. Towards an objective of increased participation and broader impacts, this program will bring together nationally recruited students and those from UMN and local institutions to establish a cohort with diverse academic and cultural backgrounds. A Big 10 University situated in a large urban environment, UMN has a strong research community that encourages transdisciplinary research within and outside the boundaries of the institution.Closely mentored by a member of the Computer Science and Engineering (CS&E) faculty, each student will contribute to active research that addresses open questions in computational complexity, machine learning, parallel and distributed computing, mobile and cloud computing, or graphics and visualization. A UMN REU participant might use observation data to simulate crowd behavior, analyze genomic sequence data to better understand microbial communities, develop tools to analyze chemical-genetic interaction networks, improve spatial perception in a virtual environment, develop visualization techniques to better understand massive data sets, enhance parallel distributed processing through algorithm development or by harnessing the computational power of a network of mobile devices, or use graph-based approaches to better understand climate change. The diverse research of CS&E faculty represents collaboration across the University with faculty in genetics, chemistry, climate science, neuroscience, architecture, medicine, and biomedical engineering to propel all of these disciplines and computer science towards previously unattainable insights and discoveries. The web site http://www-users.cs.umn.edu/~boley/big-data-reu/index.html has more information on this project.

作为明尼苏达大学（UMN）REU网站计划的参与者，学生将参与研究，开发由大数据驱动的跨学科科学发现的计算方法。 在这个为期10周的暑期课程中，除了沉浸在研究中，学生还将接受技术培训和专业发展，鼓励他们为科学事业做好准备。 这包括大数据座谈会，传播科学研讨会，职业指导和研究成果的公开传播。为了提高参与度和更广泛的影响力，该计划将汇集全国招募的学生和来自UMN和当地机构的学生，以建立一个具有不同学术和文化背景的群体。作为一所位于大城市环境中的十大大学，UMN拥有强大的研究社区，鼓励机构内外的跨学科研究。在计算机科学与工程（CS E）教师的密切指导&下，每个学生都将致力于积极的研究，解决计算复杂性，机器学习，并行和分布式计算，移动的和云计算，或者图形和可视化。UMN REU参与者可能会使用观察数据来模拟人群行为，分析基因组序列数据以更好地了解微生物群落，开发工具来分析化学-遗传相互作用网络，改善虚拟环境中的空间感知，开发可视化技术以更好地理解海量数据集，通过算法开发或通过利用移动的设备网络的计算能力来增强并行分布式处理，或者使用基于图表的方法来更好地理解气候变化。CS E教师的多样化研究代表了整个大学与遗传学，化学，气候科学，神经科学，建筑，医学和生物医学工程教师的合作，以推动所有这些学科和计算机科学实现以前无法实现的见解和发现。 网站http://www-users.cs.umn.edu/~boley/big-data-reu/index.html有关于这个项目的更多信息。