REU Site: Computational Methods for Discovery Driven by Big Data

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.