Alan Hubbard, Ph.D.

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...

Maya Petersen, M.D. Ph.D.

Professor of Biostatistics
Epidemiology and Biostatistics

Dr. Maya L. Petersen is a Professor of Biostatistics and Epidemiology at the University of California, Berkeley. Dr. Petersen’s methodological research focuses on the development and application of novel causal inference methods to problems in health, with an emphasis on longitudinal data and adaptive treatment strategies (dynamic regimes), machine learning methods, and study design and analytic strategies for cluster randomized trials. She is a Founding Editor of the Journal of Causal Inference and serves on the editorial board of ...

Mark van der Laan, Ph.D.

Professor of Biostatistics
Biostatistics and Statistics

Mark van der Laan is the Jiann-Ping Hsu/Karl E. Peace Professor of Biostatistics and Statistics at the University of California, Berkeley. He graduated in 1993 under supervision of Richard Gill at the Utrecht University in the Netherlands. He started a position in Biostatistics in 1994 and has been at UC Berkeley since. He has made contributions to survival analysis, semiparametric statistics, multiple testing, censored data and causal inference. He also developed the targeted maximum likelihood methodology and general theory for super-learning. He is a founding editor of...