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Research Experience for Undergraduates

Data-Driven Security

UPDATE: Due to the current public health situation, the department of Computer Science will not offer this nine-week summer undergraduate research program in 2020. Please find more information about this program below and we hope to see you apply next year!

Data-driven Security members

Data-driven security is an emerging interdisciplinary field that applies data science and artificial intelligence to mitigate cyber-attacks and other security risks and threats. Undergraduate students will participate in summer research activities with faculty mentors from the computer science and mathematics disciplines. The students will work in interdisciplinary teams to explore important research questions and will also participate in other professional development activities that will prepare them for future careers in the computing fields.

We welcome applications from current undergraduate students at national institutions with strong interests in computer science, mathematics, technology, or engineering. We encourage applications from students who are also members of a group underrepresented in STEM fields or who are enrolled in a minority, women’s, or non-doctoral institution. The REU program will award up to ten NSF fellowships.

We invite Boise State undergraduate students to apply to our program. A limited number of fellowships are available to Boise State students.

PIs:

    • Francesca Spezzano, PhD
    • Edoardo Serra, PhD

Visit the REU Data-driven Security website


REU Sites

Computer and Information Science and Engineering (CISE)

View the latest REU CISE opportunities across the US at the NSF site


Past REU Opportunities:


Software Security

REU Software Security group photo

Previous “Software Security” Research Experiences for Undergraduates were held during the summer of 2015, 2016, and 2017 as a nine-week program on the BSU campus.  This research sponsored by the National Science Foundation.

The goal was to offer motivated undergraduate students from Boise State and other institutions across the nation with a competitive research experience in software security.

During each summer, the program hosted ten students, five from Boise State and five from other institutions, to conduct research with active and experienced research faculty. Through the research experiences, students improved their ability to formulate and solve a research problem, and increase their communication proficiency in both a scientific and community context. Each student received a total stipend of $4,500 ($500/week for 9 weeks), reimbursement of the travel expenses for a round-trip from their home school to Boise State, and paid housing and meals.

2015 Student REU presentation photo

The nine-week summer program schedule was as follows: In the first two weeks, students focused on developing projects. Tutorials and workshops were held to help students establish necessary background knowledge and learn about how to conduct scientific research. Students also worked with their mentors, read literature, and designed their projects. Week 3 – 8, students carried out their projects under the supervision of their mentors. They also attended seminars by faculty and invited industry speakers and report their weekly progress. In week 9, students prepared a written report in the format of a scientific paper and an oral presentation modeled after a scientific meeting. Field trips to local industry and social events were scheduled throughout the summer program.  For example, 2015 REU Participants Amy Ferris (Whitworth University) and Ali Sellsted (Washington State University) present their research poster “Optimization and Assessment of Path Constraints in Symbolic Execution”

PIs:

    • Dr. Dianxiang Xu, Secure Software Development, Access Control, Security of Software Defined Networks
    • Dr. Jyh-Haw Yeh, Data Privacy and Integrity, Applied Cryptology
    • Dr. Elena Sherman, Software Quality Assurance
    • Dr. Gaby Dagher, Applied Cryptography, Information Security, Data Privacy and Cloud Computing
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