Pathways 2024 participants

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
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Undergraduate Programs

Our undergraduate curricula are aligned with the ACM & IEEE Curriculum Guidelines. Courses in artificial intelligence, machine learning, computer vision, and human-centered computing. Experiential learning provides students with a practical and rounded curriculum.

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Graduate Programs

We offer M.S. and Ph.D. degrees in Computer Science and Statistics, a graduate certificate in Cyber Security, and a PSM degree in Cyber Security. For selected full-time students, the department may offer teaching and research assistantships that pay a stipend and cover tuition, fees, and insurance.

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Research

As our department encompasses both computer science and statistics, the areas of research are wide-ranging. Faculty and students collaborate across URI with industry and government.

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

International Program

We collaborate with URI’s International Engineering Program (IEP) and the Department of Languages to offer the unique International Computer Science Program (ICSP).

Announcements

  • Hoon Cho [Talk] Hoon Cho: Enabling Collaborative Genomic Studies with Privacy (4/15/2026) - When: Friday, April 17, 3:00 PMWhere: Tyler 055 AbstractThe sensitive nature of genomic data poses major challenges for data sharing and collaboration in biomedicine. Traditional safeguards often lead to fragmentation across data silos, hindering large-scale analysis. I will describe our recent work on secure federated (SF) algorithms, which combine cryptography and distributed computation to enable […]
  • Shaun Wallace Shaun Wallace Named 2026 URI SSIREP Public Policy Fellow (4/3/2026) - Assistant Professor Shaun Wallace has been selected as a 2026 Public Policy Fellow through URI’s Social Science Institute for Research, Education, and Policy (SSIREP). Wallace’s fellowship project, “Exploring Cyber Dating Aggression in Real-Time Among Young Adults,” will prototype a web-based user-first privacy-preserving extraction pipeline for identifying cyber dating aggression (CDA) from their naturally occurring digital […]
  • Anny-Claude Joseph [Talk] Anny-Claude Joseph: Causal Inference under Spatial Interference (4/2/2026) - When: Friday, April 10, 3:00 PMWhere: Tyler 055 AbstractEnvironmental epidemiologists are increasingly interested in establishing causality between exposures and health outcomes. A popular model for causal inference is the Rubin Causal Model (RCM). An important assumption under RCM is no interference, that is, the potential outcomes of one unit in the study are not affected […]
  • IACR [Talk] Data and Discussion DS event: Academic and Professional Opportunities (3/31/2026) - When: Friday, April 3, 12-2 pm Where: LIB 166 Join us for an engaging Data Science event co-hosted with the Women in Data Science club. Our featured speaker is Alena Korshunova (MBA), a Principal Business Intelligence Analyst in Innovation, Analytics & AI at FM Global. She will share insights into her career path and experience […]
  • Ryan Fox-Tyler [Talk] Ryan Fox-Tyler: AI Agents in Production: The Gap Between What’s Possible and What’s Deployable (3/30/2026) - When: Friday, April 3, 3:00 PMWhere: Tyler 055 AbstractEvery generation of developer infrastructure faces the same core tension: how do you give increasingly powerful systems the ability to act autonomously while maintaining the safety and governance guarantees that organizations require? For decades, this played out in distributed systems — microservices, data pipelines, and platform engineering […]
  • Optimization Example [Talk] LicketySPLIT: Near-Optimal Decision Trees in a SPLIT Second (3/27/2026) - When: Tuesday, March 31, 11:00 AMWhere: Bliss Hall 190 AbstractDecision tree optimization is fundamental to interpretable machine learning. The most popular approach is to greedily search for the best feature at every decision point, which is fast but provably suboptimal. Recent approaches find the global optimum using branch and bound with dynamic programming, showing substantial […]
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