Statistical Modelling for Data Science (Bayesian Statistics, Machine Learning, Statistical Modelling)
Howard Bondell is Professor of Statistical Data Science, ARC Future Fellow, and Deputy Head of the School of Mathematics and Statistics at the University of Melbourne, where he has been since 2018. Prof Bondell received his Ph.D. in Statistics from Rutgers University in 2005 and immediately commenced his academic career in the Department of Statistics at North Carolina State University. He was elected Fellow of the American Statistical Association in 2017. His current research interests include: model selection, robust estimation, regularisation, Bayesian methods, and all aspects of modelling and handling uncertainty in statistical and machine learning approaches.
Rutgers, The State University of New Jersey 2005
My research focuses on statistical modelling approaches in Data Science. Statistical Data Science deals with the the mathematical development of methods to characterise and account for uncertainty via both Bayesian and Frequentist approaches. I am interested in both methodological developments as well as applications.