The Montana State University Algorithms Research Experience for Undergraduates (REU) is a ten-week residential program in the summer of 2023 that focuses on collaborative algorithm development and problem solving for topics in the broad themes of optimization and sustainability with specific topics ranging from computational biology and geometry to optimization of infrastructure networks. Participants will work with each other and faculty members to develop their research skills and work to solve novel problems.

Program Details:
  • May 29 - August 4 (10 weeks)
  • $6,000 stipend
  • Travel alowance
  • $120 per week food allowance
  • Free on-campus housing
  • Social activities


Applications for the Summer of 2023 are currently being accepted. Applications will be accepted until April 10th, 2023, with acceptance decisions being made on a rolling basis. We strongly encourage American Indian students to apply. Please indicate in your application materials if you belong to a federally recognized tribe.

Participant Requirements:
  • NSF requires applicants to be citizens or permanent residents of the United States.
  • Applicants must be undergraduate students in good standing.
  • Applicants must have completed a course in Algorithms and programming.
Applicaitons can be submitted online via the NSF's ETAP system by following this link: NSF ETAP. The following applications material are required:
  • Undergraduate transcript (unofficial is ok)
  • 1-2 letters of recommendation from college professors/instructors
  • Personal statement detailing:
    • Your interest in the program
    • Career aspirations
    • The (ranked) top three projects discussed below you are interested in working on.


Montana State University is located in the beautiful town of Bozeman, Montana. As the epicenter of the Northern Rocky Mountains, there are endless outdoor activities on the doorstep including access to Yellowstone National Park a mere 80 miles away. Bozeman also hosts many community events in the summer that are easily accessible from campus including Shakespeare in the park, music on Main Street, and farmer's markets. Montana State University hosts many high-tech laboratory and comfortable work spaces with diverse summer dining options.


Participants in this program will partake in organized seminars and workshops to aid the development of their research skills. The cornerstone of the program is working on a project with another participant, with close mentoring from a faculty member. These projects will be centered on applied algorithms, but the application fields are quite diverse. Possible projects include:

Network Optimization

This project will develop novel network optimization techniques to help design viable CO2 capture and storage (CCS) infrastructure. CCS is a critical tool for mitigating climate change and needs to be widely deployed in the very near future by many industries. Designing massive CCS infrastructure is a complicated network optimization problem that requires careful and comprehensive planning to ensure decisions are made in a cost-effective manner.

Computational Geometry

Combinatorial and geometric optimization is certainly a traditional problem. A recent trend in geometric optimization is to consider more realistic constraints, for instance, putting facilities (e.g., new stores) on line segments (modeling streets) such that the facilities are far away. On the other hand, computing the genomic distance between two genomes (sequences) A and B, i.e., a minimum number of allowed genomic operations (say reversals) to convert A to B, has been researched extensively since early 1990s. On the other hand, most of these genomic operations are content-preserving (e.g., reversals, which are among the generally called rearrangement operations). We have started to explore new genomic operations, e.g., tandem duplications.

DNA/RNA Assembly

The assembly of full RNA and DNA sequences from short sequence data is a fundamental computational problem in biology. A highly successful approach to addressing assembly problems has been to express them abstractly in terms of computational problems on graphs, such as finding a minimum path cover or decomposing flow in a network. This project builds on this approach to develop state-of-the-art algorithmic approaches to better assemble sequences with guarantees on computational efficiency and the quality of the assemblies produced.

Contact Information

  • Dr. Sean Yaw, Program Director,
  • Dr. Brendan Mumey, Program Co-director,
  • Dr. Binhai Zhu, Program Co-director,