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.

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International Computer Science Program student, Richard Burke standing in front of the Brandenburg gate during his year abroad in Germany

Announcements and Jobs

  • New England Computer Science Teachers Association Conference (3/21/2023) - The New England chapter of the Computer Science Teachers Association (CSTA) is coming to UConn at Storrs on October 20, 2023. Educators of all grade levels, including post-secondary, education leaders, library media specialists, administrators, coaches, school counselors, and researchers are welcome to join in the fun! We will focus on BUILDING CS PATHWAYS. Keynote speakers […]
  • Haihan Yu, “A Composite Empirical Likelihood Method for Time Series in Frequency Domain Inference” (2/24/2023) - When: Mar 3rd, 11:00-12:00 Where: Tyler 053 Zoom link: https://uri-edu.zoom.us/my/guangyuzhu Abstract: Frequency domain analysis of time series is often difficult, as periodogram-based statistics involve non-linear averages with complicated variances. Due to the latter, nonparametric approximations from resampling or empirical likelihood (EL) are useful. However, current versions of periodogram-based EL for time series are highly restricted: […]
  • Matthew Wascher, “Monitoring disease prevalence and transmission in a population under repeated testing” (2/24/2023) - When: Mar 2nd, 14:00-15:00 Where: Tyler 053, Zoom link: https://uri-edu.zoom.us/my/guangyuzhu Abstract: In this talk, I will describe a statistical methodology developed as part of the COVID-19 monitoring efforts of The Ohio State University (OSU) and which is designed for monitoring disease transmission using repeated testing data. Under a repeated testing scheme in which individuals who […]
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