Mark van der Laan

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

His research focuses on developing statistical methods for causal and non-causal parameter estimation from complex data structures, particularly through his pioneering work on Targeted Maximum Likelihood Estimation (TMLE) and super learning methodologies. His work bridges advanced statistical theory with practical applications in biomedical research, genomics, and public health.

Van der Laan's recent publications demonstrate a strong focus on causal inference methodology applied to real-world medical problems, including long COVID research, nutritional epidemiology, and genomic medicine. His work consistently emphasizes the integration of machine learning with causal inference frameworks to address challenges in high-dimensional data analysis.

Scientific Awards:

• COPSS Presidents' Award (2005)
• Mortimer Spiegelman Award (2004)
• van Dantzig Award (2005)

As an educator, van der Laan has mentored numerous students through his teaching of advanced courses including PH C240B Biostatistical Methods: Survival Analysis and Causality, PH 243A Multivariate Statistical Methods in Genomics, and PH 296 Causal Inference Seminar. His research group actively develops R packages to translate theoretical methods into practical tools for the research community.

He maintains active collaborations with researchers at UC San Francisco, Kaiser Permanente, and the FDA Real World Evidence Group, focusing on applying advanced causal inference methods to solve pressing public health challenges.