REU Site: Computational Methods for Discovery Driven by Big Data

作为明尼苏达大学(UMN)REU Site项目的参与者，学生们将从事研究，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周的暑期计划中，除了沉浸在研究中，学生还将接受技术培训和专业发展，以鼓励和准备他们在科学领域的持续职业生涯。这包括大数据研讨会、传播科学研讨会、职业指导和研究成果的公开传播。为了实现更多的参与和更广泛的影响，该计划将把国家招收的学生以及来自曼彻斯特大学和当地机构的学生聚集在一起，建立一个具有不同学术和文化背景的队列。作为一所位于大城市环境中的十大大学，密歇根大学拥有一个强大的研究社区，鼓励机构内外的跨学科研究。在计算机科学与工程(CS&amp；E)教员的密切指导下，每个学生都将为解决计算复杂性、机器学习、并行和分布式计算、移动和云计算或图形和可视化方面的开放问题的积极研究做出贡献。UMN REU参与者可以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，或者使用基于图形的方法来更好地了解气候变化。CS&Amp；E教职员工的多样化研究代表着整个大学与遗传学、化学、气候科学、神经科学、建筑学、医学和生物医学工程方面的教职员工的合作，以推动所有这些学科和计算机科学朝着以前无法实现的见解和发现迈进。网站http://www-users.cs.umn.edu/~boley/big-data-reu/index.html上有关于这个项目的更多信息。

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.