DBB Research Areas
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The Department of Bioinformatics and Biostatistics is committed to maintaining a robust research program, both in the development of methodology in bioinformatics and biostatistics and in collaborative engagements with researchers at the University of Louisville and beyond. Our faculty, staff and students conduct methodological research in a diverse set of areas, including Bayesian inference, bioinformatics and statistical genetics, clinical trials, causal inference, functional data analysis, survival analysis and modern statistical computing.
Through collaborative engagement initiatives, our faculty partner with researchers in numerous areas including public health, medicine, nursing, dentistry, psychology and engineering providing biostatistical expertise in support of scientific development within and outside the University. Our Seminar Series provides weekly seminars covering the cutting-edge research conducted by faculty within and outside the University.
To learn more about the research being conducted by specific faculty members, visit our faculty page or contact us.
Faculty Research Areas
Jeremy Gaskins
Biostatistics, Bayesian methodology, variable selection, longitudinal data, Markov chain Monte Carlo, statistical collaboration
Bakeerathan Gunaratnam
Clinical trials, longitudinal analysis
Shih-Ting Huang
Deep learning, robust statistics, precision medicine, high-dimensional statistics, machine learning, image analysis
Maiying Kong
Experimental design, data analyses, clinical trials, causal inferences, spatiotemporal data analysis.
K.B. Kulasekera
Personalized medicine, nonparametric statistics, smoothing methods, regression techniques
Doug Lorenz
Clinical prediction models, child abuse detection
High dimensional data, health equity, longitudinal studies, next-generation sequencing, Bayesian
Artificial intelligence; causal inference; deep learning; functional/longitudinal data analysis; genomics data; imaging data analysis; non-parametric/semi-parametric model; mediation analysis; high-dimensional model.
Probability modeling, Bayesian inference, cancer screening, sensitivity, sojourn time, lead time, overdiagnosis, scheduling, computing, clinical trials.
Survival analysis, high dimensional data, statistical learning, causal inference, health policy, study design, power analysis, collaborative research