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] Efficient Gaussian Process Surrogates for Blackbox Optimization and Posterior Approximation (2/18/2026) - When: Friday, February 20, 11:00 AM Where: Pharmacy 240 Abstract In this talk, we explore efficient Gaussian process surrogate modeling in two distinct contexts: bandit optimization and blackbox posterior approximation. For optimization, we propose novel noise-free Bayesian optimization strategies that incorporate a random exploration step to enhance the accuracy of Gaussian process surrogate models. The […]
[News] Hack@URI to hold Hackathon Weekend (2/13/2026) - Hack@URI will hold a Hackathon Weekend on Saturday, February 21 and Sunday, February 22. Hack@URI says its mission is to bridge the gap between classroom learning and real-world experience, and organizers say they hope to inspire innovation and problem-solving and connect participants with the Rhode Island tech and creative community. The event will start at […]
[Talk] Travess Smalley: Generative Systems in Art & Design (2/12/2026) - When: Friday, February 20, 3:00 PM Where: Tyler 055 Abstract Generative Systems in Art and Design is an artist talk that surveys Travess Smalley’s generative practice, including scripting in Adobe Photoshop, creative coding, and building small creative software. It also looks at ways software-based systems can be translated into physical prints and exhibition work. The […]
[Award] Faculty publication awarded IEEE Editor’s Choice (2/12/2026) - IEEE Access designated Game Theoretic Mixed Experts for Combinational Adversarial Machine Learning by Kaleel Mahmood, Ethan Rathbun, Ronak Sahu, Marten Van Dijk, Sohaib Ahmad, and Caiwen Ding as an Editor’s Choice article. The publication is currently listed on the Featured Articles page.
[Talk] Lesia Semenova: Which Model Should You Trust When Many Models Fit? (2/11/2026) - When: Thursday, February 12, 12:00 PM Where: Bliss 190 Abstract In practice, there is rarely a single “golden” answer. In this talk, I argue that trustworthy ML should be set-valued: instead of validating a single model, we should reason over the Rashomon set (the set of models that meet a performance criterion). I’ll present a […]
[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 […]