Mahlet Tadesse, ScD, MS

ProfessorMahlet T
Department of Mathematics and Statistics
Georgetown University


Fax 202-687-6067

Research Interests

My research focuses on the development of statistical and computational tools for the analysis of large-scale genomic data. I am particularly interested in stochastic search methods and Bayesian inferential strategies to identify structures and relationships in high-dimensional data sets. Some research problems I am currently working on include: (1) identification of biologically relevant markers and prediction of clinical outcomes in a unified manner, (2) integration of various genomic data sources, (3) integration of biological knowledge in the evaluation of genomic data. I am also involved in various collaborative public health projects.  In particular, I have been working with the GRAPE team on perinatal projects focused on elucidating biological mechanisms underlying adverse birth and maternal outcomes in pregnancies.  In addition, over the past few years, I have been involved as a faculty mentor in the Harvard Multidisciplinary International Research Training (MIRT/MHIRT) Program.

Selected Publications

  • Lee KH, Tadesse MG, Baccarelli AA, Schwartz J, Coull BA (2017) Multivariate Bayesian variable selection exploiting dependence structure among outcomes: Application to air pollution effects on DNA methylation. Biometrics, 73: 232-241
  • Denis M, Tadesse MG (2016) Evaluation of hierarchical models for integrative genomic analyses. Bioinformatics, doi:10.1093/bioinformatics/btv653.
  • Tsai T-H, Tadesse MG, Di Poto C, Pannell LK, Mechref Y, Wang Y, Ressom HW. Multi-profile Bayesian alignment model for LC-MS data analysis with integration of internal standards. Bioinformatics. 2013, 29: 2774-2780.
  • Stingo FC, Chen YA, Tadesse MG, Vannucci M. Incorporating biological information into linear models: A Bayesian approach to the selection of pathways and genes. Annals of Applied Statistics. 2011, 5: 1978-2002. PDF
  • Monni S and Tadesse MG. A stochastic partitioning method to associate high-dimensional responses and covariates (with discussion). Bayesian Analysis. 2009, 4: 413-436.
  • Kim S, Tadesse MG, Vannucci M. Variable selection in clustering via Dirichlet process mixture models. Biometrika. 2006, 93: 877-893.
  • Tadesse MG, Sha N, Vannucci M. Bayesian variable selection in clustering high-dimensional data. Journal of the American Statistical Association. 2005, 100: 602-617. PDF