Draft:Mark Van der Laan, Ph.D

Mark Johannes van der Laan, Ph.D

Mark Johannes van der Laan is a Dutch-American biostatistician. He is currently a Professor of Biostatistics and Statistics at the University of California, Berkeley, where he holds the position of the Jiann-Ping Hsu/Karl E. Peace Endowed Chair in Biostatistics. He is known for his contributions to causal inference and statistical methodology, particularly Targeted Learning. Developed in response to challenges dealing with the curse of dimensionality and the complexity of real-world data, Targeted Learning is subfield of statistics applicable across a variety of applications, including the analysis of clinical trials, assessment of (causal) effects in observational and real-world evidence studies, and the analysis of high-dimensional and multi-modal data.

Early Life and Education

Van der Laan was born on July 4, 1967 in the Netherlands, son of Ann and Paul van der Laan, a professor of Statistics. During his youth, he was a competitive chess and tennis player. He also exhibited an early interest in mathematics and statistics, and pursued a joint bachelor’s and master's degree in Mathematics at the University of Utrecht, specializing in Statistics. During his master’s studies, he spent a year at North Carolina State University, Raleigh, studying at the Department of Statistics and playing on the university’s tennis team.

Mark’s master's thesis, guided by Professor Richard D. Gill, focused on the Dabrowska Estimator and the Functional Delta method. Van der Laan furthered his education at the Department of Mathematics at Utrecht University, as a doctoral student under Professor Richard D. Gill, and completed part of his research at the University of California, Berkeley, under Professor Peter J. Bickel. His doctoral thesis, "Efficient Estimation in the Bivariate Censoring Model," was defended in 1993.

A highlight of Van der Laan’s thesis is the development of the first efficient estimator of the bivariate survival function based on bivariate right-censored failure time data. The main idea was to regularize the nonparametric maximum likelihood estimator (NPMLE) through artificial extra censoring. This work was further generalized to develop the first regularized NPMLE of a full-data distribution based on general censored data. Another key contribution was an identity for the NPMLE that allowed for an elegant proof of asymptotic efficiency of the NPMLE under minimal conditions.

[We will add the math for that identity here]

Career

Van der Laan's academic career began at the University of California, Berkeley in 1994 as an Assistant Professor of Biostatistics. He ascended through the ranks to become a full Professor in 2000, holding joint appointments in the School of Public Health and the Department of Statistics. He served as the Chair of the Group of Biostatistics from 2018 to 2024. Since its inception in 2020, he has been the co-Director of the Center for Targeted Machine Learning and Causal Inference (CTML) at the University of California, Berkeley. Van der Laan is also the co-founder of TL Revolution, an enterprise consulting and software solutions company, grounded in Targeted Learning.

'''Research and Contributions '''

Mark J. van der Laan's research is extensive and interdisciplinary, combining rigorous statistical theory with innovative applications and causal inference. His contributions embody a profound commitment to advancing statistical science in the service of public health and medical research. They have not only enriched the field of biostatistics but have also had a tangible impact on the broader scientific community. Van der Laan’s contributions can be divided into several key areas:

1. Development of Statistical Methodologies: Van der Laan has pioneered in developing statistical methodologies aimed at analyzing high-dimensional censored longitudinal data derived from observational, real-world data studies and randomized clinical trials. His work is characterized by the development and application of semiparametric statistical theory to solve problems in complex data structures arising in the real world.

2. Causal Inference: He has made substantial contributions to the field of causal inference, particularly in the context of longitudinal studies. Van der Laan's research in this area has focused on developing methods that account for informative treatment assignment and informative censoring, which are common challenges in clinical and epidemiological research.

3. Adaptive Designs and Surveillance Systems: Van der Laan has been instrumental in advancing adaptive designs within clinical trials. His research includes the development of targeted adaptive designs with corresponding targeted maximum likelihood estimators that allow for more flexible and efficient trial designs, while preserving the robust unbiased inference of RCTs, thereby enhancing the ability to make timely and accurate decisions during the trial process.

4. Machine Learning Algorithms: Among his notable contributions is the development of the Highly Adaptive Lasso (HAL) algorithm. HAL represents a new class of machine learning algorithms that has remarkable theoretical statistical properties, such as dimension free rates of convergence, pointwise asymptotic normality and asymptotic efficiency for plug-in estimation of smooth features of the target function. It has shown promise in many applications, including data-driven prediction models, conditional density estimation, conditional treatment effect estimation, intensity estimation, and variable selection in high-dimensional and multi-modal data settings.

5. Targeted Learning: Perhaps one of Van der Laan's most significant contributions is the development of Targeted Learning, a framework that combines the strengths of machine learning and traditional statistical inference. The cornerstone of this approach is the Targeted Maximum Likelihood Estimation (TMLE), which provides a robust, flexible methodology for estimating causal effects and parameters in complex models. This approach is designed to reduce bias and improve efficiency, making it particularly suitable for observational data and complex longitudinal studies.

6. Collaborative Research and Software Development: Van der Laan actively engages in collaborative research, working with interdisciplinary scientists across the world to apply Targeted Learning to real-world problems. He has also contributed to the development of several software packages and publicly available educational materials, making his methodologies accessible to a wider research community.

Awards and Honors

Throughout his career, Van der Laan has received numerous awards and honors, including the Mortimer Spiegelman Award for outstanding contributions to health statistics; the van Dantzig Prize, the highest award in Statistics and Decision Theory in the Netherlands; and the COPSS Presidents' Award for outstanding contributions to the statistics profession. His work has been recognized globally, with invitations to keynote talks and lectureships worldwide.

'''Personal Life '''

Van der Laan is married to Martine, and they have three children: Laura, Lars, and Robin.

Publications and Editorial Work

Van der Laan has authored numerous influential papers and books, significantly contributing to the literature in biostatistics and statistical methodology. He serves on the editorial boards of several prestigious journals and has been an associate editor for notable publications such as Annals of Statistics, Biometrics and the Journal of the American Statistical Association. He is co-founder of the International Journal of Biostatistics and the Journal of Causal Inference.

Most Recent Publications:

For all of Mark van der Laan’s work, please visit his Google Scholar.

Teaching and Mentorship

As an educator, Van der Laan has taught a wide range of courses at UC Berkeley, from introductory statistics, survival analysis, adaptive designs, multiple testing, to advanced statistical theory and causal inference. He has mentored over 55 Ph.D. students and 20 postdoctoral fellows, many of whom have gone on to make significant contributions in academia, industry, and public health.

External Links Faculty page at UC Berkeley Research profile on Research.com