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.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.Mark came to UC Berkeley from the Netherlands' University of Utrecht, where he studied mathematics (1985-1990) and obtained his Ph.D. (1993). He completed his thesis, "Efficient and inefficient estimation in semiparametric models," under the guidance of Prof. Richard D. Gill.In 1994, Mark was a Neyman Visiting Professor in the Statistics Department at UC Berkeley. Subsequently, he accepted a tenure track position in the Division of Biostatistics, School of Public Health at Berkeley, and became an Associate Professor in Biostatistics in July 1997. By July 2000, Mark was a Professor in Biostatistics and Statistics.