Alan Hubbard, Ph.D.

Job title: 
Professor of Biostatistics

My research focuses on the application of statistics to population studies with particular expertise in semi-parametric models and the use of machine learning in causal inference, as well as applications in high dimensional biology. Applied work ranges from the molecular biology of aging, wildlife biology, social epidemiology, infectious disease and acute trauma. I am particularly interested in harnessing machine-learning algorithms and advances in semiparametric causal inference towards machines for optimizing the estimation of parameters related to causal inference/variable importance, with particular emphasis on discovering and estimating the impact of treatment rules. In addition, currently exploring the application of data-adaptive target parameter approaches to formalize asymptotics for exploratory data analysis, to allow for a lack of a priori specified hypotheses while still providing an estimation of meaningful parameters and estimators with predictable sampling distributions.

Research interests: 
  • Targeted Learning
  • Causal inference
  • Machine learning
  • Statistical issues in epidemiology
  • Precision medicine and public health