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Research Experience for Undergraduates: Data-driven Security

The department of Computer Science offers a nine-week summer undergraduate research program in data-driven security.

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 2020 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.

Application deadline: March 1, 2020.

Visit the REU Data-driven Security website

Research Areas

Engineering

Software Engineering

EFC: “The application of a systematic, disciplined, quantifiable approach to the development, operation, and maintenance of software”—IEEE Standard Glossary of Software Engineering Terminology, IEEE std 610.12-1990, 1990.

Faculty: Bogdan DitElena ShermanJim Buffenbarger

Computational Science and Engineering

EFC: Computational Science and Engineering (CSE) is an exciting and rapidly evolving field that exploits the power of computation as an approach to major challenges on the frontiers of natural and social science and all engineering fields. In keeping with Harvard’s emphasis on foundational knowledge, CSE degree programs focus on cross-cutting mathematical and computational principles important across disciplines. – Harvard University

Cathie OlschanowskyMin Long

Data and Analysis

Informational Retrieval

EFC: Information retrieval (IR) is the science of searching for information in documents, searching for documents themselves, searching for metadata which describe documents, or searching within hypertext collections such as the Internet or intranets. – Wikipedia

Faculty: Casey Kennington, Michael Ekstrand

Big Data and Data Science

EFC: Data Science is the field that comprises of everything that is related to data cleansing, data mining, data preparation, and data analysis. Big Data refers to the vast volume of data that is difficult to store and process in real-time. This data can be used to analyze insights which can lead to better decision making. – Quora

Faculty: Amit JainCasey KenningtonEdoardo SerraFrancesca SpezzanoSteven Cutchin

Data Mining

EFC: Data mining is used to discover patterns and relationships in the data in order to help make better business decisions. Data mining can help spot sales trends, develop smarter marketing campaigns, and accurately predict customer loyalty.

Edoardo SerraGaby Dagher

Social Analysis and Mining

EFC: Social Network Analysis and MiningSocial Network Analysis and Mining (SNAM) is a multidisciplinary journal serving researchers and practitioners in academia and industry.

Francesca Spezzano

Research Centers, Labs, and REU Programs

Explore the CS department specialized labs and research centers.