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.

A three-dimensional manifold

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

  • 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 […]
  • Laura Forastiere [Talk] Laura Forastiere: Maximizing effectiveness under constrained resources through policy targeting under heterogeneous spillover effects (3/13/2026) - When: Friday, March 27, 3:00 PMWhere: Tyler 055 AbstractIn many empirical settings, individuals are interconnected, and an individual’s outcome may depend on the treatment of others, leading to interference. When interference is heterogeneous, treating individuals with specific characteristics can influence the population average outcome differently, either through their direct response or their impact on others. […]
  • Alina Jade Barnett [Talk] Alina Jade Barnett: Inherently Interpretable Deep Learning Models (3/5/2026) - When: Friday, March 13, 3:00 PM Where: Tyler 055 Abstract AI models now perform high-stakes tasks traditionally reserved for skilled professionals, often surpassing human expert performance. Despite these advances, the “black box” (i.e., uninterpretable) nature of many machine learning algorithms poses significant challenges. Opaque models resist troubleshooting, cannot justify their decisions, and lack accountability—limitations that […]
  • [Talk] Chenyang Zhong: Faithful and Efficient Synthetic Data Generation via Penalized Optimal Transport Network (2/27/2026) - When: Wednesday, March 4, 3:00 PM Where: Pharmacy 240 Abstract The generation of synthetic data whose distributions faithfully emulate the true data-generating mechanism is of critical importance in modern statistics and data science, with applications ranging from systematic model evaluation to augmenting limited datasets. While Wasserstein Generative Adversarial Networks have shown promise in this area, […]
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