The School of Information at the University of Arizona offers the following primary research areas: 

Information Collections, Libraries, Databases, and Archives: A number of faculty members research topics as digital archives, artifact preservation, information organization, museums, institutions of cultural heritage, and or databases across business, sciences, and other contexts. Information curation, organization, management, and use is at the core at what most of our faculty do.

Machine learning, Artificial Intelligence, and Algorithmic Thinking: Some in the School develop machine learning/artificial intelligence algorithms, engage in Bayesian modeling/inference and unsupervised learning algorithms. These faculty enjoy teaching computers to do things ranging from understanding scientific text, to playing music, to learning temporal emotional interactions within personal relationships.

Applied Natural Language Processing: Several faculty members develop useful NLP methods to solve information organization problems, from evaluating information quality of depression websites, automated structuring of museum labels, to converting descriptive text into tabular formats (information extraction). This line of research focuses on the organization of unstructured text of a variety of types.

Biolological informatics: Some are actively involved with medical information text mining for cancer signaling networks, text mining and biological ontology construction in biological taxonomic literature, development of biological ontologies and standards (e.g., Biodiversity Information Standards, formerly TDWG) and research in other biological informatics areas, enabling things like semi-automated interactive key generation, ecological niche modeling, and machine reasoning over text. 

Social Network Analysis, Internet Studies, and Issues of Society: These researchers focus on societal concerns tied to such topics as art or performance, ethics, human rights and related policies, issues of diversity or information access, surveillance, privacy, work practices, economies, networks and human behavior. While some write about issues of value or philosophy and culture, others engage in creative computing and coding in the arts. Still others rely on data, engaging in data science, computational social science or qualitative/discourse analyses of data to research people doing things like learning, purchasing, marketing, seeking support from others in online communities, or finding information. 

College of Social and Behavioral Sciences