The department hired a cohort of new faculty with specialized expertise in the materials and methods of data science for social research. They graduated from one the top programs in their disciplines, bring a unique technical expertise to the field, and provide substantive expertise across a variety of topical domains. This cohort of new faculty with complementary skills provide an immediate breadth and depth to graduate and undergraduate data science training, cross-department and cross-college collaborations, and the potential for the department to become a regional leader in computational social science.
Dr. AJ Alvero uses computational tools and frameworks to study society. His current research agenda focuses on the relationships between textual artifacts, systems of evaluation, and national discourse from various perspectives. This includes research using natural language processing to show how college admission essays encode identity, culture, and demographics at-scale.
Dr. Alvero received the B.A. in English from the University of Miami, an M.S. in Foreign Language Education from Florida International University, an M.S. in Statistics from Stanford University, and a Graduate Certificate in Computational Social Science from Stanford University. Dr. Alvero completed the Ph.D. in Education Data Science/Sociology of Education from Stanford University.
Dr. Won-tak Joo holds research interests in social demography, social networks, and computational sociology. Specifically Dr. Joo examines how social and family relationships change across the life course, and how these patterns contribute to social and health inequalities, using a range of computational skills including social network methods, applied econometrics, and demographic techniques.
Dr. Won-tak Joo received the B.C. in Mass Communication, the B.A. in Sociology and the M.A. in Sociology from Yonsei University, Korea. Dr. Joo completed the Ph.D. in Sociology from the University of Wisconsin-Madison. Before arriving at the University of Florida, Dr. Joo completed a Postdoctoral Fellowship in Demography at the University of California-Berkeley.
Dr. Lin Liu uses random forests and decision tree tools of machine-learning to study barriers to successful reentry and reintegration into society after being released from prison. In particular, she focuses on determining the factors associated with employment three months after release across multiple states and communities, drawing important policy implications such as tax incentive programs.
Dr. Liu received a B.A. in Law from Hebei University of Technology, China, an M.A. in Law from the University of Science and Technology, Beijing China, and an M.S. In Statistics and an M.A. and Ph.D. in Criminology from the University of Delaware. She joins the department in Fall 2023.
Dr. Edo Navot’s research in the fields of stratification, organizational studies, and economic sociology focuses on the quantitative analysis of income inequality. He uses machine-learning to examine the potential discriminatory impact of AI-based hiring and promotion algorithms.
Dr. Navot received the B.A. in Economics from New York University and the M.Phil. in Economics from the New School for Social Research. Dr. Navot completed the Ph.D. in Sociology from the University of Wisconsin-Madison. Before arriving at the University of Florida, Dr. Navot worked as a labor economist for the U.S. Department of Labor.