Coronavirus Information
As we work together to battle the coronavirus, we will continue to offer safe and secure online sessions . Even though our physical office is closed, in accordance with the guidelines recommended by CDC, we are working remotely and continuing to provide student, staff, and faculty assistance. We can be reached Monday-Friday 9am-4pm Mountain Standard Time at 520-621-3565 or by email – please refer to the iSchool Directory. Please allow up to 24 hours response time. Faculty and Adjuncts will respond as their schedules permit.
Get COVID-19 updates and information for the University of Arizona community. Also, see SBS resources for continuing instruction and learning.
Research Labs
The School of Information research labs function as places for scholarly work, student interaction, and discovery.
Astrolabe
The Astrolabe Project is working with CyVerse cyberinfrastructure to provide an easy-to-use, reliable and long-lived environment for processing, storage and visualization of valuable scientific data that are not managed as part of existing missions and trusted repositories. Particular emphasis is given to individual researchers and research groups with older, orphaned datasets that can add context to new research enterprises.
Biosemantics Research Group
The Biosemantic Research Group focuses on methods for producing computable FAIR data in biodiversity domains, covering research in information extraction, controlled vocabulary/ontology construction, knowledge modeling, software development and user experience evaluation.
Computational Language Understanding Lab
The Computational Language Understanding Lab at University of Arizona is a team of faculty, students, and research programmers who work together to build systems that extract meaning from natural language texts, including question answering (answering natural language questions), information extraction (extracting specific relations and events), semantic role labeling (extracting semantic frames that model who did what to whom, when and where), parsing the discourse structure of complex texts, and other computational linguistics problems.
Digital Storytelling and Oral History Lab
The Digital Storytelling and Oral History (DS|OH) Lab communicates digital storytelling and oral history research to a broader public as engaged research and, importantly, as a vehicle for social justice. DS|OH Lab does this by providing scholars and community members with hands-on training in critical media skills as well as in a diverse set of research methods to include participatory and feminist action research methods and those community-based methods that emerge through decolonizing methodologies.
Extended Reality and Games Lab
The Extended Reality and Games Lab (XRG Lab) performs research on enhanced extended (virtual and augmented) reality systems and novel interaction techniques for improved usability and user experience. Our work mainly consists of design, development and evaluation (through empirical user studies) of these interaction techniques and enhanced systems.
Machine Learning and Artificial Intelligence Lab
The Machine Learning and Artificial Intelligence Lab applies state-of-the-art methods in computational intelligence to research problems that span disciplinary boundaries. We are working to model human cognitive development in silico, with robots or softbots in game environments as the “babies” being raised or trained. Other research interests include the sensorimotor foundations of human language; several projects in the last decade have developed algorithms for sensor-to-symbol kinds of processing in service of learning the meanings of words, most recently, verbs; and Education Informatics, which includes intelligent tutoring systems, data mining and statistical modeling of students’ mastery and engagement, assessment technologies, ontologies for representing student data and standards for content, architectures for content delivery, and so on. There are many opportunities to develop and apply AI technologies to provide high-quality education for all students (e.g., K12@UA, Teach Ourselves).
