My research focuses on the application of statistics to population studies with particular expertise in semi-parametric models and the use of machine learning in causal inference, as well as applications in high dimensional biology. Applied work ranges from the molecular biology of aging, wildlife biology, social epidemiology, infectious disease and acute trauma. I am particularly interested in harnessing machine-learning algorithms and advances in semiparametric causal inference towards machines for optimizing the estimation of parameters related to causal inference/variable...
Maya L. Petersen, MD, PhD is Professor of Biostatistics and Epidemiology (UC Berkeley) and of Computational Precision Health (UCSF and UC Berkeley), the co-Director of the UCSF-UC Berkeley Joint Program in Computational Precision Health, and the co-Director of UC Berkeley’s Center for Targeted Machine Learning and Causal Inference. Dr. Petersen’s methodological research sits at the intersection of AI, statistical inference, and causal inference, with...
Mark van der Laan is the Jiann-Ping Hsu/Karl E. Peace Professor of Biostatistics and Statistics at the University of California, Berkeley. He graduated in 1993 under supervision of Richard Gill at the Utrecht University in the Netherlands. He started a position in Biostatistics in 1994 and has been at UC Berkeley since. He has made contributions to survival analysis, semiparametric statistics, multiple testing, censored data and causal inference. He also developed the targeted maximum likelihood methodology and general theory for super-learning. He is a founding editor of...
