Laura Ridenour is an Assistant Professor in the School of Information Science and Learning Technologies. Her research examines how knowledge is created, organized, represented, and discovered, focusing on the intersections of data science, artificial intelligence, information retrieval, and knowledge organization. Drawing on the science of science and the philosophy of information, she uses data-driven methods to uncover the hidden stories embedded in data, information systems, and the classification systems that shape how people understand the world.

Her current research focuses on three areas: 1) the use of artificial intelligence for public good, particularly with regard to making open data more accessible and easier to use; 2) the unintended consequences of semantics and conceptual neighborhoods surrounding neurodivergent diagnoses, and how editorial and classificatory changes influence both public- and self-perception of disabilities; and 3) how ongoing technological evolution has reshaped the fundamental principles of scholarly communication, attribution, and stewardship of the scholarly record.

She is currently Principal Investigator on an IMLS-funded grant exploring the use of AI to support the documentation, understanding, use, retrieval, and visualization of Open Government Data available through APIs. Dr. Ridenour integrates her research into her teaching, using real-world data and technologies to help students understand their applications, implications, and significance for the information professions.

She is currently accepting Ph.D students; please include an attached CV (not a resume) and the word “trek” in the subject line.

Affiliations

• Association for Information Science and Technology
• Association for Library and Information Science Education
• International Society for Knowledge Organization (ISKO)
• International Society for Knowledge Organization, Scientific and Technical Advisory Committee, Working Group on AI
• Quantum Innovation Center (QIC)
• Data, Science, and Society

Areas of Expertise

• Ethical AI for public good
• Data ethics
• Data science
• Knowledge organization
• Information retrieval
• Scholarly communication
• Open data
• Computational social science
• Science of science