Assistant Professor
Fode.Tounkara@osumc.edu
Before joining the Ohio State University, Dr. Tounkara was an Assistant Professor within the Department of Statistical Sciences at the University of Toronto, where he taught undergraduate and graduate courses in statistics including regression analysis, statistical theory, survey analysis, and sampling techniques. He also held a position as a biostatistician in the Lunenfeld Tanenbaum Research Institute at the University of Toronto, where he used the copula approach for modeling cancer risk in Hereditary Breast Cancer Syndrome families. His current work includes a machine learning (high-dimensional statistics) project to develop statistical approaches that can properly handle predictors with weak signals in high-dimensional data.Copula models for correlated outcomes and applications in cluster randomized trials
Machine learning methods for variable selections and predictions in high dimensional data, application in cancer omics data
Joint Modeling of Longitudinal and Time-to-Event outcomes
Casual Interence
OSU James Comprehensive Cancer Center
Fode.Tounkara@osumc.edu
Biography
Dr. Tounkara is an Assistant Professor in the Department of Biomedical Informatics at The Ohio State University. As a biostatistician, his expertise includes statistical methods for clustered and correlated data. His PhD thesis involved the development of a new statistical model framework for analyzing clustered binary data, while his postdoctoral research in statistical genetics at McGill University involved the development of a flexible model to analyze secondary phenotypes from data collected through case-control, extreme-trait, and multiple-trait designs. He received his PhD from Université Laval in Quebec, Canada.Before joining the Ohio State University, Dr. Tounkara was an Assistant Professor within the Department of Statistical Sciences at the University of Toronto, where he taught undergraduate and graduate courses in statistics including regression analysis, statistical theory, survey analysis, and sampling techniques. He also held a position as a biostatistician in the Lunenfeld Tanenbaum Research Institute at the University of Toronto, where he used the copula approach for modeling cancer risk in Hereditary Breast Cancer Syndrome families. His current work includes a machine learning (high-dimensional statistics) project to develop statistical approaches that can properly handle predictors with weak signals in high-dimensional data.
Education and Training
Post-Doctoral, McGill University, 2016PhD, Applied Mathematics (Statistics), Université Laval, 2015
M.S., Statistics, Université Joseph Fourier, France, 2010
M.S., Applied Mathematics, Université Gaston Berger, 2008