Mark van der Laan

Mark van der Laan

Title
Jiann-Ping Hsu E. Peace Professor of Biostatistics
Department
Berkeley Public Health
Dept of Statistics
Phone
(510) 643-9866
Fax
(510) 643-5163
Research Expertise and Interest
targeted learning, real-world data integration in RCTs, sequential adaptive designs, computational biology and genomics, censored data and survival analysis, medical research, inference in longitudinal studies
Research Description

Mark van der Laan is the Jiann-Ping Hsu/Karl E. Peace Professor of Biostatistics and Statistics at the University of California, Berkeley and co-Director of the Center for Targeted Machine Learning and Causal Inference (CTML) Research Center at University of California, Berkeley. He also serves as Director of the Computational Biology core of the University of California, Berkeley Superfund Research Program.

As the principal investigator on several NIH-funded and foundation grants, Mark has worked on and developed topics such as super learning (an ensemble machine learning estimation approach), targeted maximum likelihood estimation (TMLE), and collaborated on targeted maximum likelihood estimation (CTMLE). He was a co-PI for the T32 Biomedical Big Data Training Grant based at UC Berkeley and has made contributions to survival analysis, semiparametric statistics, multiple testing, censored data, and causal inference. He also has extensive experience in translating theoretical work into practically useful software R-packages.

Mark is a founding editor of the Journal of Causal Inference and International Journal of Biostatistics. He has authored 4 books on targeted learning, censored data, and multiple testing, and authored over 300 publications. He received the COPSS Presidents' Award in 2005, the Mortimer Spiegelman Award in 2004, and the van Dantzig Award in 2005, among various others. Currently, he has an active research partnership with multiple investigators at UC San Francisco and is a Statistical Consultant for Kaiser Permanente Safety Analysis and the FDA Safety Analysis Group.

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