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Jeremy Gaskins, PhD
Bioinformatics and Biostatistics
Associate Professor
485 E. Gray St., Room 130
Louisville, KY 40202
Phone: (502) 852-3300
Fax: (502) 852-3294
jeremy.gaskins@louisville.edu
Background
Ph.D. (2013) Statistics, University of Florida
B.S. (2007) Mathematics and Applied Mathematics, Auburn University
I joined the Department of Bioinformatics & Biostatistics at UofL in the fall of 2013 at the rank of Assistant Professor and was promoted to Associate Professor with Tenure in July 2019.
Research Interests
My main methodological interest is the application of Bayesian methods to gain insight from complex data sets. In particular, I focus on situations where data may be longitudinal, be subject to missing values (both informative and non-informative), have complex dependence structures, and/or require joint modelling of mixed data types. A key component of much of my work also includes exploring relevant computational strategies to facilitate Bayesian inference in these complicated model scenarios. Additionally, I maintain active research collaborations regarding medical and public health research. I work regularly with researchers from the Depts of OB/GYN, Radiation Oncology, Surgery, and various other groups with UofL.
Teaching
PHST 661 - Probability
PHST 662 - Mathematical Statistics
Selected Methodological Manuscripts:
Gaskins, J.T., M.J. Daniels. (2013) A nonparametric prior for simultaneous covariance estimation. Biometrika, 100(1): 111-124. PMID: 24324281
Gaskins, J.T., M.J. Daniels, B.H. Marcus. (2014) Sparsity inducing prior distributions for correlation matrices of longitudinal data. Journal of Computational and Graphical Statistics. 23(4):966-984. PMID: 25382958
Gaskins, J.T., M.J. Daniels, and B.H. Marcus. (2016) Bayesian methods for non-ignorable dropout in joint models in smoking cessation studies. Journal of the American Statistical Association, 111(516): 1454-1465. PMID: 29104333
Gaskins, J.T., and M.J. Daniels. (2016) Covariance partition priors: A Bayesian approach to simultaneous covariance estimation for longitudinal data. Journal of Computational and Graphical Statistics. 25(1): 167-186. PMID: 27175055
Choo-Wosoba, H., J.T. Gaskins, S.M. Levy, S. Datta. (2018) A Bayesian approach for analyzing zero-inflated clustered count data with dispersion. Statistics in Medicine, 37:801-812. PMID: 29108124
Wu, Y., J.T. Gaskins, M. Kong, S. Datta. (2018) Profiling the Effects of Short Time-course Cold Ischemia on Tumor Protein Phosphorylation using a Bayesian Approach. Biometrics, 74(1): 331-341. PMID: 28742267
Gaskins, J.T. (2019) Hyper Markov laws for correlation matrices. Statistica Sinica, 29: 165-184.
Sekula, M., J.T. Gaskins, S. Datta. (2019) Detection of differentially expressed genes in discrete single cell RNA sequencing data using a hurdle model with correlated random effects. Biometrics¸ 75(4):1051-1062. PMID: 31009065
Shah, J., G. Brock, J.T. Gaskins. (2019) BayesMetab: Treatment of Missing Values in Metabolomic Studies using a Bayesian Modeling Approach. BMC Bioinformatics, 20:673.
Ghosal, S. T. Lau, J.T. Gaskins, M. Kong. (2020) Hierarchical Mixed Effect Hurdle Model for Time and Spatially Correlated Count Data and its Application to Identifying Factors Impacting Health Professional Shortages. Journal of the Royal Statistical Society: Series C,69(5):1121-1144.
Sekula, M., J.T. Gaskins, S. Datta. (2020) A sparse Bayesian factor model for the construction of gene co-expression networks from single-cell RNA. BMC Bioinformatics. 21:361.
Kang, T., J.T. Gaskins, S. Levy, S. Datta. (2021) A Longitudinal Bayesian Mixed Effects Model with Hurdle Conway-Maxwell-Poisson Distribution. Statistics in Medicine. 40(6):1336-1356.
Kundu, D., R. Mitra, J.T. Gaskins. (2021) Bayesian Variable Selection for Multi-Outcome Models Through Shared Shrinkage. Scandinavian Journal of Statistics. 48(1):295-320.
Sekula, M., J.T. Gaskins, S. Datta. (2022) Single-cell Differential Network Analysis with Sparse Bayesian Factor Models. Frontiers in Genetics. 12:810816
Pal, S., J.T. Gaskins. (2022) Modified Polya-Gamma Data Augmentation for Bayesian Analysis of Directional Data. Journal of Statistical Computation and Simulation, 92(16):3430-3451.
Uddin, M.N., J.T. Gaskins. (2023) Shared Bayesian Variable Shrinkage in Multinomial Logistic Regression. Computational Statistics and Data Analysis, 177:107568.
Gaskins, J.T., C. Fuentes, R. de la Cruz. (2023) A Bayesian Nonparametric Model for Classification of Longitudinal Profiles. Biostatistics, 24(1):209-225.
Kang, T., J.T. Gaskins, S. Levy, S. Datta. (2023) Analyzing Dental Fluorosis Data using a Novel Bayesian Model for Clustered Longitudinal Ordinal Outcomes with an Inflated Category. Statistics in Medicine, 42(6):745-760.
Kundu, D., R. Mitra, P. Albert, J.T. Gaskins. (2023) A Bayesian Hierarchical Sparse Factor Model for Estimating Simultaneous Covariance Matrices for Gestational Outcomes in Consecutive Pregnancies. Statistics in Medicine, 42(19): 3353-3370.
Wu, D., J.T. Gaskins, M. Sekula, S. Datta. (2023) Inferring Cell-Cell Communications from Spatially Resolved Transcriptomics Data using a Bayesian Tweedie Model. Genes. 14(7): 1368.
Gaskins, J.T., M.J. Daniels. (2013) A nonparametric prior for simultaneous covariance estimation. Biometrika, 100(1): 111-124.
Gaskins, J.T., M.J. Daniels, B.H. Marcus. (2014) Sparsity inducing prior distributions for correlation matrices of longitudinal data. Journal of Computational and Graphical Statistics. 23(4):966-984.
Gaskins, J.T., M.J. Daniels, and B.H. Marcus. (2016) Bayesian methods for non-ignorable dropout in joint models in smoking cessation studies. Journal of the American Statistical Association, 111(516): 1454-1465.
Gaskins, J.T., and M.J. Daniels. (2016) Covariance partition priors: A Bayesian approach to simultaneous covariance estimation for longitudinal data. Journal of Computational and Graphical Statistics. 25(1): 167-186.
Choo-Wosoba, H., J.T. Gaskins, S.M. Levy, S. Datta. (2018) A Bayesian approach for analyzing zero-inflated clustered count data with dispersion. Statistics in Medicine, 37:801-812.
Wu, Y., J.T. Gaskins, M. Kong, S. Datta. (2018) Profiling the Effects of Short Time-course Cold Ischemia on Tumor Protein Phosphorylation using a Bayesian Approach. Biometrics, 74(1): 331-341.
Gaskins, J.T. (2019) Hyper Markov laws for correlation matrices. Statistica Sinica, 29: 165-184.
Sekula, M. J.T. Gaskins, S. Datta. (2019) Detection of differentially expressed genes in discrete single cell RNA sequencing data using a hurdle model with correlated random effects. Biometrics¸ Pre-print available online.
Daniels, M.J., and J.T. Gaskins. (2013) Bayesian methods for the analysis of mixed categorical and continuous (incomplete) data. In Analysis of Mixed Data: Methods and Applications (edited by A.R. de Leon and K. Carriere Chough). pg. 189-208. Chapman & Hall/CRC Press
