Dr. Justin Luningham
Assistant Professor, Department of Population & Community Health
Education & Experience:
I received my Master of Science in Applied and Computational Mathematics and Statistics (ACMS) and my PhD in Quantitative Psychology from the University of Notre Dame. I completed a Postdoctoral Fellowship in Computational and Quantitative Genetics in the Department of Human Genetics at Emory School of Medicine in October 2019. Prior to joining the UNTHSC School of Public Health, I served as Assistant Research Professor of Biostatistics in the School of Public Health at Georgia State University.
Teaching Areas & Public Health Interests:
I teach introductory and advanced courses in applied statistics and research methods. My overarching aim is to teach students how to think critically about data and the best way to address their particular research questions, rather than simply teaching how to perform an analysis with a particular software program. I believe that an emphasis on real-world application is crucial for today’s students, in addition to understanding statistical inference and developing skills with computational packages.
Professional Activities & Awards:
I am a member of the American Statistical Association and the Hierarchical Taxonomy of Psychopathology Consortium, and I have previously been active in the Behavior Genetics Association, the Psychometric Society, and the American Psychological Association. I am an active reviewer for journals in statistics, public health, and the social sciences.
Scholarly Interests:
My primary methodological research involves statistical and computational genetics, longitudinal data analysis, and integrative data analysis. I am interested in using large-scale genetic data and computational statistics to better understand the genetic basis of diseases and mental health disorders. I have developed and applied techniques to better understand how multiple mental health outcomes (for example, depression and anxiety) correlate at the genetic level.
I use statistical methods to understand how people change over time and develop methods to better measure those changes. For example, I have applied my longitudinal data analysis expertise to analyze bi-directional effects between parents and adolescents as they develop over time and to identify racial disparities in predictors of breast cancer mortality.
Integrative data analysis (IDA) refers to a broad framework for combining different datasets and data types across multiple independent studies for joint analysis. My previous research evaluated different latent variable approaches for performing IDA and used IDA to model aggressive behaviors in children and adolescents across several multi-national cohorts. I am interested in continued development of data integration procedures using latent variable models and missing data imputation approaches.
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