The Department of Computer Science and Statistics supports the work of scholars and students who are adept in taking an agile, interdisciplinary approach to solving problems. As our department encompasses both computer science and statistics, the areas of research specialization are wide-ranging.
In computer science, they cover the areas of algorithms, accessibility, computer algebra, computational biology, CS education, computer vision, cyber security, data science/machine learning, databases, digital forensics, human-computer interaction, gaming, programming languages, and simulation.
In statistics, these research interests include adaptive statistical methods, sequential statistics, Bayesian optimal experimental designs, statistical decision theory, statistical analysis of networks, multivariate statistical learning, longitudinal modeling, missing data, online-updating big data, survival analysis, computational statistics, latent class modeling, statistical genetics, and space time data analysis.
The Accessible and Socially Aware Technologies (ASSET) lab explores the frontiers of research in computer science in our quest to build technologies for marginalized populations. The lab works in a multidisciplinary space at the intersection of accessibility, human-computer interaction, and applied machine learning, often taking inspiration from ideas, theories, and methodologies from the humanities and social sciences to understand and address technical problems.
URI Algorithms for Big Data Research Group
My research group focuses on algorithms and data structures for “big data,” with applications to computational biology and more recently astronomy, among other areas. We are particularly interested in the “manifold hypothesis” and how interesting geometric and topological properties of data can enable more efficient algorithms for search and analysis. We are also interested in computational topology, and approaches for making topological data analysis tractable on large data sets.
Our research interests are centered on big data and applied AI with a focus on smart cities and smart health related applications. Our work has been recognized by a number of research awards and our research is sponsored by funds from NSF, TIDC, and USDA.
The Human-Centered Experiential Technologies (HAX) lab follows a user-centered design process to create and maintain public research systems to augment human information interactions. We are human-computer interaction system researchers applying methods from social computing and human-centered AI to create high-quality data and personalized solutions. We continuously improve our real-world systems to attract, motivate, and support everyday users in the wild.
We study how ML works for science, technology, and society. This means we apply data science using ML models to understand the world with other scientists, study and evaluate those ML systems directly and build tools to help them intervene. Most of our work right now focuses on building more fair AI and some includes collaboration with other scientists.
The lab’s current and past research are in the broad area of Machine Learning, with particular emphasis on graph-structured data. The lab’s work has been applied to challenging problems arising in a variety of domains, including computer program optimization and analysis, computer vision, and computational biology.
Computational Statistics and Machine Learning
A group that creates, applies, and improves new techniques for the analysis of data including, algorithmic and statistical techniques, as well as newly merged computational and statistical techniques.
Cryptology, the science of coding and decoding secret messages, is divided into cryptography, which concerns designing cryptosystems, and cryptanalysis, which is concerned with breaking cryptosystems. Our research has resulted in the creation of an educational website and a Maple® package for cryptography.
Interactive 3D Graphics Partnership
A special interest group of faculty, staff, and students, involved in the research, development, teaching, and cross-disciplinary utilization of 3-dimensional modeling, animation, and interactivity. We offer courses coordinated between the Art and Computer Science Departments. Our research projects are defined by interdisciplinary collaboration.
The NCIPHER team is applying the tools of causal inference and network science to address some of the most pressing public health and educational challenges, such as harm reduction measures among people who use drugs, increasing pre-exposure prophylaxis uptake among men who have sex with men, and improving learning in statistics education. We recognize in these contexts that shared influence between patients, students, and participants must be considered when evaluating interventions with possible dissemination.
Many faculty members in the Department of Computer Science and Statistics work on substantial research projects with undergraduate students. The undergraduate research page provides a sample of the types of projects of current undergraduate research.undergraduate research
- Timothy Colaneri - Can you tell us a little bit about your background, other interests, hobbies, etc? I am a lifelong resident of Newport, RI and commute to campus. I am a non-traditional student born in 1985. I transferred to URI after having finished the AS computer science program at CCRI. Inside of academics, my interests lie in […]