Alan Hubbard, PhD

Associate Professor, School of Public Health

Bio

Alan Hubbard, Professor of Biostatistics at UC Berkeley and Head of the Division of Biostatistics, works on an estimation of complex causal parameters and prediction algorithms using machine learning, with an emphasis on applications in epidemiology, environmental exposure and biomedicine.

Biography

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

Education

PhD – Biostatistics
University of California, Berkeley, 1998

MS – Geology & Paleontology
Virginia Polytechnic University, 1990

BA – Geology
University of California, Santa Barbara, 1985

Publications

Google Scholar

PubMed

Courses Taught

PH240A
Theoretical Biostatistics

PH 242C
Longitudinal Data Analysis

BBD Seminar and Capstone

PH241
Statistics of Epidemiology