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

  • 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 […]
  • Alina Barnett, Inherently Interpretable Neural Networks for Scientific Discovery and High-Stakes Decision Support (3/1/2025) - When: Tuesday, 3/4 11 am – 12 pm. Where: Tyler Hall 055. Abstract Artificial intelligence is increasingly performing high-stakes tasks traditionally reserved for skilled professionals, with AI systems often surpassing human expert performance on specific tasks. Despite these advances, the “black box” (i.e., uninterpretable) nature of many machine learning algorithms poses significant challenges. These opaque […]
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