Faculty Research

What is a R designation?

One way of describing an institution is by its “R” designation. Research universities are categorized as R1, R2, and doctoral/professional (formerly R3) by the Carnegie Classification of Institutions of Higher Education. To be an R1 or R2, the school must award more than 20 research or scholarship-based doctorates and have a research budget of more than $5 million per year. The University of South Florida is classified as an R1 university with “very high research activity.” This page highlights recent developments and works of research from iSchool faculty.

SI Faculty ResearcH

 Dr. Ly Dinh

Research Area - Computational social science, network science, organizational communication, crisis informatics 

Ly’s research focuses on the formation and evolution of network structures in various social systems including friendship networks, multi-teams systems, research organizations, and large-scale emergency response operations. Ly also develops and tests new measures to capture the complexities of social interactions that can be observed at multiple levels of analysis. Her work provides 1) empirical evidence in support (and sometimes, in opposition) of network theories in various social and organizational contexts, and 2) refined methods to measure network dynamics that were previously difficult to capture (e.g. interdisciplinary ties, balance for signed and directed connections). Ly’s research has appeared in Communication Research, Scientific Reports, Communication Studies, and peer-reviewed outlets in computational social science. Ly is a recipient of the 2020 Grace Hopper Scholar for Women in Computing, and a 2018 Network Science Fellow at the Visible Networks Labs.

Dr. Alon Friedman

Research Area - Data Visualization

Alon Friedman, Ph.D., is an interdisciplinary researcher with a focus on visualization theory and visualization education. His research examines different theoretical perspectives to organize data through visualization built under open-source software lenses. Some of his current projects include analyzing visualizations using Peirce's sign theory algorithms, utilizing Mark Lombardi's lens with big data, and developing Visual Peer Review. He received a National Science Foundation award under EHR (Education and Human Resources) for improving and advancing critical Peer Review. He has published in IEEE Vis Journal and conference proceedings, the Journal of Biochemistry and Molecular Biology Education, and the Journal of Information Visualization.

Dr. Stephen "Scuba" Gary

Research Area - Cyber Intelligence and Advanced Persistent Threats

Cyber threats are a constant in our networked world. Infrastructure such as electrical power and telecommunications are often cited as targets of cyberattacks but in the post-COVID world, health, and economic systems have been and will be targeted by malicious actors. Through this research, Dr. Gary defines cyber intelligence and provides examples for how it is collected and used by governments and the private sector along with what traits, core competencies, and skills are required by the workforce. Dr. Gary's work also highlights examples of how cybersecurity, information security, cyber intelligence, and intelligence are taught in academia. It delineates reasons why cyber intelligence is more important than ever in a post-pandemic world. 

Dr. Loni Hagen

Research Area - Big Data Analytics, Social Network Analysis and Machine Learning

Data scientists from the computing disciplines often focus their efforts primarily on optimizing algorithms and mathematical models. Human-centered data science, however, works from the assumption that computational models do not necessarily “speak for themselves”; that optimal and actionable results (results to be applied in the “real world”) are best achieved when human judgments are added to the interpretive equation. The most vexing issues in big data analytics today are not in data analysis, but in interpreting, translating, and applying the results. As the demand for data-driven insights increases across industries in the public and private sectors, the need to integrate social scientists in the big data life cycle have exploded. However, few, if any, methodological guidelines exist for using and validating analytics results. To address this knowledge gap in the literature, my publication goals focused specifically on generating guidelines for government practitioners, social scientists, and humanists on “how to apply, validate, and interpret” big data analytics.

Dr. Kim Lersch

Research Area:  Spatial data science 

 Kim Lersch, Ph.D., integrates the use of geographic information systems and spatial data analysis to explore the distribution of violent crime, suicides and mental health issues, and other social problems. Her current research projects include a spatiotemporal analysis of domestic disturbance 911 calls for service during COVID lockdowns, and a risk terrain analysis of sexual assaults. She has published several books, including Geographies of Behavioral Health, Crime and Disorder (2020, edited collection with J. Chakraborty); Space, Time, and Crime (2020; 5th ed., with T. Hart & M. Chataway); and Policing and Misconduct (2002; edited collection) and over 50 journal articles, book chapters, and technical reports.   

Dr. Kathleen de la Peña McCook

Research Area - Public Libraries, Cultural Heritage

Dr. Kathleen de la Peña McCook is a distinguished university professor. Her current research is the preservation and destruction of memory across institutions—libraries, museums, and intangible cultural heritage. She also focuses on how human rights and community engagement inform public library services. She is currently working on the American Library Association “Standards for Library Services for Individuals Who Are Incarcerated or Detained” Task Force. McCook is the author of Introduction to Public Librarianship (American Library Association).

Dr. Jinfang Niu

Research Area - Archives

Current research is being done on how technologies transform archives management practices, theories and workforce, and make recommendations for how archival education programs, professional associations, and commercial service providers should adapt to the shifting archives management landscape.