REU Site: Computational Methods for Discovery Driven by Big Data

该项目的目标是继续明尼苏达大学（UMN）本科生研究经验（REU）网站，学生参与研究，开发由大数据驱动的跨学科科学发现的计算方法。由计算机科学与工程（CS E）教师密切指导，每个学生将有助于积极研究，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，或图形和可视化的开放问题。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。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。为了实现增加参与度和更广泛影响的目标，该计划将汇集全国招募的学生以及来自巫统和当地机构的学生，建立一个具有不同学术和文化背景的群体。http://reubigdata.cs.umn.edu/The 明尼苏达大学（UMN）REU网站计划的目标是（i）智力参与和激发参与者，以激励他们对科学事业的承诺和追求，特别是促进学术坚持，（ii）增加妇女和计算机科学中代表性不足的少数民族对科学的参与和贡献，（iii）培养学生对科学的持续贡献，特别是在大数据跨学科研究的计算方法，以及（iv）专业准备和指导参与者从事科学事业，即，教导参与者成为有效的沟通者，精通职业，精通科学道德。为了实现这些目标，在为期10周的暑期课程中，学生每天都沉浸在研究中，解决大数据计算方法中的开放问题。整个夏天，每个学生都由一名教师和研究生密切指导。计划活动帮助学生快速适应研究和独立工作，最重要的是，激励和准备学生的学术坚持和科学事业。活动包括研究教程，大数据座谈会系列，传播科学研讨会系列，职业指导，并在校园范围内的研究研讨会海报演示。该计划结合了非居民和居民计划，以创建一个多达25名学生的队列：2名来自当地机构，8名来自该补助金资助的国家招聘工作，15名学生通过其他资助机制。这一联合项目增加了多样性，提高了项目和影响的可持续性，并充分利用了经济效益。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The objective of this project is to continue the University of Minnesota (UMN) Research Experiences for Undergraduates (REU) Site in which students engage in research that develops computational methods for scientific discovery across disciplines that are driven by big data. Closely mentored by 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 genome 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. 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. http://reubigdata.cs.umn.edu/The objectives of the University of Minnesota (UMN) REU Site program are to (i) intellectually engage and excite participants to motivate their commitment to and pursuit of a career in the sciences, specifically to foster academic persistence, (ii) increase participation in and contribution to the sciences by women and underrepresented minorities in computer science, (iii) train students for sustained contribution to the sciences, particularly in computational methods for big data transdisciplinary research, and (iv) professionally prepare and mentor participants for a career in the sciences, i.e., to teach participants to be effective communicators, be career savvy, and versed in the ethics of science. Towards these objectives, in a 10-week summer program students are immersed daily in research addressing open questions in computational methods for big data. Throughout the summer, each student is closely mentored by a faculty member and graduate student. Program activities help students quickly acclimatize to research and independent work, and most importantly, motivate and prepare students for academic persistence and a career in the sciences. Activities include research tutorials, a Big Data Colloquium series, a Communicating Science workshop series, career mentoring, and a poster presentation at a campus-wide research symposium. The program combines a non-resident and resident program to create a cohort of up to 25 students: 2 from local institutions, 8 from a national recruiting effort funded by this grant, and 15 students through other funding mechanisms. This combined program increases diversity, improves program and impact sustainability, and capitalizes on economic efficiencies.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.