Dr. Mohammad Tabatabai is a tenured Professor and Director of Biostatistics at Meharry Medical College in Nashville, Tenessee. In addition, He serves as a member of the data science core for the Tennessee Center for AIDS Research (TN-CFAR) and a member of Publications Committee for Southern Community Cohort Study (SCCS) at Vanderbilt University. His methodological research in biostatistics includes Hypertabastic survival models https://en.wikipedia.org/wiki/Hypertabastic_survival_models, Hyperbolastic functions and categorical regressions and their applications in tumor growth, stem cell proliferation and machine learning methods https://en.wikipedia.org/wiki/Hyperbolastic_functions, robust statistical techniques such as robust correlations and similarity measures and their applications in genomics, robust generalized linear and nonlinear regressions, and longitudinal mixed models. His current research includes the effect of periodontal treatment on reducing pro-inflammatory cytokine and chemokine levels in virally suppressed HIV+ African Americans, the influence of homelessness on the viral load suppression of individuals diagnosed with HIV, the effect of histological subtypes on the survival of melanoma patients, the impact of PM2.5 on cardiometabolic disease, and the application of Hyperbolastic functions and Hypertabastic survival methods in cancer research such as, kidney carcinoma, cervical, breast, and lung cancers. He has published over 100 articles in peer reviewed journals and has been honored as the recipient of several prestigious teaching awards. He has developed novel statistical methods that are currently being used by researchers in very many areas of medical, biomedical and public health across the globe. A recent statistical package named Taba R package https://cran.r-project.org/web/packages/Taba/Taba.pdf  has been introduced to calculate robust correlations, similarity measures, partial, semi-partial, and generalized partial correlations. Taba similarity measure of this package was used in the analysis of William Syndrome. https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-021-04098-4.pdf