There are no boundaries

You are not afraid to think outside your comfort zone and stick with a problem until you find a solution. We'll help prepare you to be a collaborator, an algorithmic thinker, and a data-fluent innovator who will thrive in a rapidly changing field.

The Department of Computer Science and Statistics provides a supportive, well-integrated center of multidisciplinary learning and research. Our faculty integrate computer science, statistics, data science, and cybersecurity while reaching beyond departmental boundaries to collaborate with scientists, artists, health care researchers, historians, and engineers across the colleges at URI. Our students grow as professionals, scholars, and citizens because they receive a strong foundation and hands-on experience in the field.

Announcements and Jobs

  • Stephen Bach, Rigorously Benchmarking LLMs for Translating Text to Structured Planning Languages (3/19/2025) - When: Friday, 4/18/25 11:00 am; Where: Bliss 260 Abstract: Can large language models (LLMs) help with planning? And how should we even measure that ability? In this talk, I will present our work on Planetarium, a benchmark that evaluates LLMs’ ability to generate PDDL (Planning Domain Definition Language) code from natural language descriptions of planning […]
  • Hang Hua, Advancing Generative AI for Multimodal Intelligence (3/4/2025) - When: Friday, 3/7 11 am – 12 pm; Where: Tyler Hall 055. Abstract: Generative AI is transforming how machines interact with and augment human capabilities. However, achieving artificial general intelligence (AGI) requires addressing significant challenges in retrained language models (PLM) and multimodal large language models (MLLMs), including the brittleness of language model fine-tuning, imbalanced vision-language […]
  • Khaled Saifuddin, Hypergraph Learning: From Algorithms to Applications (3/3/2025) - When: Thursday, 3/6 11 am – 12 pm; Where: Tyler Hall 055 Abstract: This talk explores the advancement of Hypergraph Neural Networks (HyperGNNs) as a powerful extension of Graph Neural Networks (GNNs) to model higher-order relationships in complex systems, particularly in biomedical applications. While traditional GNNs struggle to capture higher-order intricate dependencies, HyperGNNs leverage hypergraphs […]
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