World's Best Scientists 2026 revealed!
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Mathematics
USA
2026

D-Index & Metrics

Mathematics

D-Index
82
Citations
24546
World Ranking
127
National Ranking
72

Research.com Recognitions

  • 2026 - Research.com Mathematics in United States Leader Award
  • 2025 - Research.com Mathematics in United States Leader Award
  • 2005 - COPSS Presidents' Award For his deep and sweeping contributions to several areas in theoretical and applied statistics, including multivariate survival analysis, local efficient estimation in semiparametric models, causal inference, dynamic treatment regimes, multiple testing, model selection, cross-validation loss-based estimation, and computational biology. Professor van der Laan has brought powerful statistical tools to bear on a wide array of applications involving high-dimensional data structures that often occur in epidemiology, genomics, and the medical sciences. The 2005 Presidents' Award honors Professor van der Laan for his signal success in bringing statistical rigor into many fields of the biomedical sciences.

Overview

Mark J. van der Laan is affiliated with the University of California, Berkeley in the United States. Their research primarily spans the field of mathematics, with a strong focus on statistics and probability. This includes work in several subfields such as artificial intelligence, economics and econometrics, nutrition and dietetics, and psychiatry and mental health.

The scientist's key research topics include advanced causal inference techniques, statistical methods and inference, statistical methods and Bayesian inference, and statistical methods in clinical trials. Additional areas of focus cover health systems, economic evaluations, quality of life, as well as child nutrition and water access and feeding issues.

Van der Laan has published extensively in a variety of venues. Frequent publication sources include:

  • arXiv (Cornell University)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Biometrics
  • Nature
  • The International Journal of Biostatistics

Their recent published papers include:

  • Practical considerations for specifying a super learner, 2023, International Journal of Epidemiology
  • Causal Inference for Social Network Data, 2022, Journal of the American Statistical Association
  • Early-childhood linear growth faltering in low- and middle-income countries, 2023, Nature
  • Child wasting and concurrent stunting in low- and middle-income countries, 2023, Nature
  • Causes and consequences of child growth faltering in low-resource settings, 2023, Nature

Van der Laan frequently collaborates with other researchers. Notable co-authors include:

  • Nima S. Hejazi
  • Alan Hubbard
  • Jeremy Coyle
  • Ivana Malenica
  • Andrew Mertens

The scientist received the COPSS Presidents' Award in 2005. The award recognized their contributions to theoretical and applied statistics across several domains such as multivariate survival analysis, semiparametric models, causal inference, dynamic treatment regimes, multiple testing, model selection, cross-validation loss-based estimation, and computational biology. This work is applied in fields involving high-dimensional data in epidemiology, genomics, and medical sciences.

Best Publications

  • To GEE or not to GEE: comparing population average and mixed models for estimating the associations between neighborhood risk factors and health.

    Alan E Hubbard;Jennifer Ahern;Nancy L Fleischer;Mark Van der Laan

  • Targeted Maximum Likelihood Learning

    Mark J. van der Laan;Daniel Rubin

  • Diagnosing and Responding to Violations in the Positivity Assumption

    Maya L Petersen;Kristin E Porter;Susan Gruber;Yue Wang

  • Estimation of direct causal effects.

    Maya L. Petersen;Sandra E. Sinisi;Mark J. van der Laan

  • Marginal Mean Models for Dynamic Regimes.

    S A Murphy;M J van der Laan;J M Robins

  • Mortality prediction in intensive care units with the Super ICU Learner Algorithm (SICULA): a population-based study

    Romain Pirracchio;Romain Pirracchio;Maya L Petersen;Marco Carone;Matthieu Resche Rigon

  • The relative performance of ensemble methods with deep convolutional neural networks for image classification

    Cheng Ju;Aurélien Bibaut;Mark J. van der Laan

  • Diaphragm and lubricant gel for prevention of HIV acquisition in southern African women: a randomised controlled trial

    Nancy S Padian;Ariane van der Straten;Gita Ramjee;Tsungai Chipato

  • A new partitioning around medoids algorithm

    Mark J. van der Laan;Katherine S. Pollard;Jennifer Bryan

  • Multiple Testing Procedures with Applications to Genomics

    Merrill D. Birkner;Katherine S. Pollard;Mark J. van der Laan;Sandrine Dudoit

  • Why Match? Investigating Matched Case-Control Study Designs with Causal Effect Estimation

    Sherri Rose;Mark J. van der Laan

  • Coarsening at Random: Characterizations, Conjectures, Counter-Examples

    Richard D. Gill;Mark J. van der Laan;James M. Robins

  • Influenza Vaccination and Mortality: Differentiating Vaccine Effects From Bias

    Bruce Fireman;Janelle Lee;Ned Lewis;Oliver Bembom

  • A new algorithm for hybrid hierarchical clustering with visualization and the bootstrap

    Mark J. van der Laan;Katherine S. Pollard

  • Unified Cross-Validation Methodology For Selection Among Estimators and a General Cross-Validated Adaptive Epsilon-Net Estimator: Finite Sample Oracle Inequalities and Examples

    Mark J. van der Laan;Sandrine Dudoit

  • Computationally efficient confidence intervals for cross-validated area under the ROC curve estimates

    Erin LeDell;Maya L. Petersen;Mark J. van der Laan

  • Collaborative double robust targeted maximum likelihood estimation.

    Mark J. van der Laan;Susan Gruber

  • Causal Models and Learning from Data: Integrating Causal Modeling and Statistical Estimation

    Maya L. Petersen;Mark J. van der Laan

  • Multiple Testing. Part I. Single-Step Procedures for Control of General Type I Error Rates

    Sandrine Dudoit;Mark J. van der Laan;Katherine S. Pollard

  • Super Learning: An Application to the Prediction of HIV-1 Drug Resistance *

    Sandra E. Sinisi;Eric C. Polley;Maya L. Petersen;Soo Yon Rhee

  • Augmentation procedures for control of the generalized family-wise error rate and tail probabilities for the proportion of false positives.

    Mark J. van der Laan;Sandrine Dudoit;Katherine S. Pollard

Frequent Co-Authors

Katherine S. Pollard
Katherine S. Pollard University of California, San Francisco
Michael Rosenblum
Michael Rosenblum University of California, San Francisco
Ira B. Tager
Ira B. Tager University of California, Berkeley
James M. Robins
James M. Robins Harvard University
Diane V. Havlir
Diane V. Havlir University of California, San Francisco
Edwin D. Charlebois
Edwin D. Charlebois University of California, San Francisco
Steven G. Deeks
Steven G. Deeks University of California, San Francisco
Ariane van der Straten
Ariane van der Straten University of California, San Francisco
Bruce Fireman
Bruce Fireman Kaiser Permanente
Elizabeth A. Bukusi
Elizabeth A. Bukusi University of Washington

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