Mark van der Laan, Ph.D., is a Professor of Biostatistics and Statistics at UC Berkeley. His research interests include statistical methods in genomics (i.e., computational biology), survival analysis, censored data, targeted maximum likelihood estimation in semiparametric models, causal inference, data adaptive loss-based super learning, and multiple testing.Read more
His research group developed loss-based super learning in semiparametric models, based on cross-validation, as a generic optimal tool for estimation of infinite dimensional parameters, such as nonparametric density estimation and prediction based on censored and uncensored data. Building on this super learning methodology, his research group developed targeted maximum likelihood estimation of a target parameter of the data generating distribution in semiparametric models, as a new generic optimal methodology for statistical inference. These general statistical approaches are applied across a large variety of applications such as in the analysis of clinical trials, assessment of (causal) effects in observational studies and the analysis of large genomic data sets.
