Welcome to CSta

We combine Computer Science, Statistics, AI, Data Science, and Cybersecurity to enhance multidisciplinary learning and research for undergrads and grads. Cross campus and industry collaborations involve faculty, students, scientists, artists, health care researchers, historians, and engineers.

Undergraduate & Graduate Courses

See our courses in Computer Science, Statistics, Data Science, and Cybersecurity ranging from computing foundations to theory and statistics to systems and artificial intelligence.

courses

Undergraduate Programs

Computer science is a vibrant and rapidly changing discipline. Our undergraduate curricula are aligned with the ACM & IEEE 2013 Curriculum Guidelines. Courses in machine learning, computer vision, and secure programming add to the core problem-solving expertise needed in diverse industrial settings. Experiential learning provides students a practical and rounded curriculum.

Hands at a keyboard with text on a screen

Graduate Programs

The department offers M.S. and Ph.D. degrees in Computer Science and Statistics, a graduate certificate in Cyber Security, and a PSM degree in Cyber Security. Our programs are committed to meeting the needs of both industry professionals and full-time graduate students. For selected full-time students, the department may offer teaching and research assistantships that pay a stipend and cover tuition, fees, and insurance.

Research

The Department of Computer Science and Statistics supports the work of scholars and students who are adept in taking an agile, interdisciplinary approach to solving problems. As our department encompasses both computer science and statistics, the areas of research specialization are wide-ranging.

International Computer Science Program student Richard Burke standing in front of the Brandenburg gate

International Program

Our research and academics help serve a global community. The Computer Science and Statistics Department collaborates with URI’s International Engineering Program (IEP) and the Department of Languages to offer the unique International Computer Science Program (ICSP).

Announcements

[Talk] Xiaomeng Ju: Bayesian Modeling for Functional and Matrix Data with Applications to Neuroimaging Analysis (2/9/2026) - When: Wednesday, February 18, 12:00 PM Where: Avedisian 105 Abstract Neuroimaging data present fundamental statistical challenges: they are high-dimensional and exhibit complex structures. In this talk, I present Bayesian methods developed for functional data and matrix-valued data motivated by neuroimaging applications, emphasizing interpretability, scalability, and uncertainty quantification. I first introduce Bayesian methods developed for two […]
[Talk] Jinghao Sun: Panel Data Meets Unmeasured Confounding: A Nonlinear Difference-in-Differences Framework (2/4/2026) - When: Friday, February 13, 3:00 PM Where: Tyler 055 Abstract Difference-in-differences (DiD) is a foundational tool for causal inference in panel data, widely used in policy, economics, and health research. Its appeal lies in its intuitive design and robustness to time-invariant unmeasured confounding. However, standard DiD relies on strong assumptions—particularly parallel trends—that are often violated […]
[Talk] Tian Wang: Modeling of Complex Data (1/30/2026) - When: Friday, February 6, 3:00 PM Where: Tyler 055 Abstract In this talk, we will first discuss the proposed α-separability for functional data. Functional data consist of random samples observed over a continuum, such as curves over a time range. These data often exhibit two kinds of variation: amplitude variation in the vertical direction and […]