Undergraduate Summer Research – University of Minnesota

Research Experience for Undergraduates (REU) Site
Computational Methods for Discovery Driven by Big Data

The Department of Computer Science and Engineering at the University of Minnesota is accepting applications at http://reubigdata.cs.umn.edu/apply.html and will begin review February 28, 2016 for the 2016 REU summer program on big data.

This program is focused on computational methods for finding and visualizing patterns in big data. In this 10-week program, students will participate in active research, receive technical training, learn about big data, and receive professional development. As a participant, you will be closely mentored by a Computer Science and Engineering faculty member.

Dates: June 6 – August 12, 2016
Support: $5000 stipend with housing and travel reimbursements.
Participants: Undergraduates both local and national in a computer science or related program.

We are looking for students strongly motivated to pursue opportunities that advance them academically, professionally, and personally, and who have the talent and potential to be successful in those endeavors.  U.S. citizens or permanent residents who are enrolled full time at an academic institution as part of an undergraduate program are eligible to apply. At the time of the program, students cannot have already earned a B.S. or B.A. degree. Students who are part of underrepresented minority groups in computer science and those attending institutions with limited research experience are encouraged to apply.

For more information, email reubigata@umn.edu or visit http://reubigata.cs.umn.edu, where you can find out more about these Big Data projects in which you could participate:

  • Understanding principles underlying large-scale online social systems.

  • Enhancing the realism of immersive virtual environments.

  • Analyzing the Earth system using graph-based approaches.

  • Defining dysbiosis.

  • Automated out-of-core execution of parallel message-passing applications.

  • Big data processing in mobile cloud platforms.

  • Data-Driven simulation and evaluation of virtual social behaviors.

  • Large-scale machine learning for data-driven discovery.

  • Interactive and perceptually accurate visualization of multidimensional data.

  • Methods for mining and visualization of biological networks.

  • Understanding recovery from addiction at scale.

  • Improving metropolitan-scale transportation systems with data-driven cyber-control.

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