|Peter J. Bickel
|Tze Leung Lai
|James O. Berge
|C. F. Jeff Wu
|Raymond J. Carroll
|Ian M. Johnstone
|Michael A. Newton
University of Wisconsin
|For ingenious and wide-ranging contributions to statistical theory and methodology, including Bayesian inference, the bootstrap, and tree reconstruction; for applications of statistical methodology, especially in genetics and genomics; and for outstanding training of graduate students.
Mark J. van der Laan
University of California, Berkeley School of Public Health
|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.
Harvard School of Public Health
|For fundamental contributions to statistical methodology and theory for longitudinal data and clustered survival data, including nonparametric kernel and spline methods as well as semiparametric regression methods, mixed effects models, and measurement error models. Also for statistical applications in health sciences; for outstanding editorial and professional services; for founding the highly successful ENAR mentoring workshop for junior faculty in biostatistics; and for outstanding work as a teacher and mentor.
Jeffrey S. Rosenthal
University of Toronto
|For fundamental contributions to probability theory, stochastic processes, and Markov Chain Monte Carlo (MCMC) algorithms, with applications to statistics; for seminal contributions to the theoretical underpinnings of the convergence rates of MCMC algorithms; for his prolific record of collaboration, resulting in significant publications in economics, mathematical finance, artificial intelligence, and survival analysis; for outstanding mentoring; and for his extraordinary skill at the communication of some of the deeper ideas of our discipline through the media (print, radio, and television) and through the publication of a general audience book on probability in real life which, less than 2 years after publication, is in its 6th printing.
University of Pennsylvania
|For fundamental and wide-ranging contributions to the mathematics, theory and methods of statistics, including the theory and application of wavelets, block thresholding in wavelet regression, optimality theory, adaptation in nonparametric function estimation, confidence intervals in small samples, and methods for false discovery rate control; for applications to chemical identification, medical imaging and microarray data analysis; and for outstanding contributions to the statistical profession through extensive editorial work and mentorship of students.
Johns Hopkins University
|For his wide-ranging and highly influential contributions to the theory and methodology of statistical science that underpins the analysis of time series and genomic data; for demonstrating the essential role of statistics to the advancement of modern genomic science and biotechnology; for advancing statistical practice through publication of open-source software and participation in the core development group of Bioconductor; for his leadership of multidisciplinary scientific teams; and for his outstanding contributions to statistics as an editorial board member and mentor of students and post-doctoral fellows.
David B. Dunson
|For his wide-ranging and fundamental contributions to the development of parametric and nonparametric modeling within complex Bayesian frameworks; for making significant concurrent scientific progress in machine learning through this development; for use of this methodology in substantive applications, notably in reproductive epidemiology; and for outstanding service to the profession as well as mentoring of students and post-doctoral researchers.
Division of Cancer Epidemiology & Genetics, National Cancer Institute, USA
|For outstanding contributions to the statistical sciences by ingenious methodological research with applications in epidemiology and genetics, including studies of gene-environment interactions, disease heterogeneity and genome-wide association studies; for fundamental contributions to the theory of case-control studies and complex retrospective sampling designs; for demonstrating leadership and a vision as a statistical scientist by actively collaborating in wide-ranging studies of cancer epidemiology and genetics and concurrently maintaining a vigorous methodological research program closely tied to cutting edge scientific issues; for exceptional mentoring and service to the profession and to the National Cancer Institute.
Samuel S. Kou
|For groundbreaking contributions to stochastic modeling and statistical inference in single molecule biophysics; for pioneering the -energy sampler; for fundamental contributions to Bayesian, empirical Bayes and nonparametric methods; and for outstanding service to the statistical profession and contribution to statistical education
|For extraordinarily wide-ranging and insightful contributions to the statistical sciences in the areas of Bayesian inference, computation and stochastic processes; for innovative statistical modeling and novel computational techniques for formerly intractable problems in molecular epidemiology, evolutionary medicine, phylogenetics, phylogeography, computational biology and emerging massive public health data sets; for a panoply of flexible, well-regarded and widely used statistical software tools for the applied sciences; for extensive and creative statistical applications across an impressive range of human inquiry; and for service to the profession as a prolific and dedicated mentor of the next generation of statisticians.
Martin J. Wainwright
|For fundamental and groundbreaking contributions to high-dimensional statistics, graphical modeling, machine learning, optimization and algorithms covering deep and elegant mathematical analysis as well as new methodology with wide-ranging implications for numerous applications.
John D. Storey
|For transformative and groundbreaking research on the theory, methods, and applications of inference methods, particularly significance testing applied to high-dimensional data analysis problems. For impactful development and application of statistics to modern biological and medical research. For service to the field of statistics in interdisciplinary activities.
|For fundamental and ingenious contributions to high-dimensional inference, variable selection, multiple testing, graphical models, machine learning and causal inference for heterogeneous data.
Tyler J. VanderWeele
Harvard T.H. Chan School
of Public Health
|For fundamental contributions to causal inference and the understanding of causal mechanisms; for profound advancement of epidemiologic theory and methods and the application of statistics throughout medical and social sciences; and for excellent service to the profession including exceptional contributions to teaching, mentoring, and bridging many academic disciplines with statistics.
Richard J. Samworth
University of Cambridge
|For fundamental contributions to nonparametric inference under shape constraints, nonparametric classification, high-dimensional variable selection change point estimation; for many substantial contributions to the profession including editorial service, extensive service to statistical societies; and for the training and mentoring of junior researchers.
|For influential work in statistical computing, visualization, graphics, and data analysis; for developing and implementing an impressively comprehensive computational infrastructure for data analysis through R software; for making statistical thinking and computing accessible to large audience; and for enhancing an appreciation for the important role of statistics among data scientists.
Rina Foygel Barber
University of Chicago
|For fundamental contributions to statistical sparsity and selective inference in high-dimensional problems, for the creative and novel knockoff filter to cope with correlated coefficients, for contributions to compressed sensing, the jackknife, and conformal predictive inference; for the encouragement and training of graduate and undergraduate students.
Johns Hopkins Bloomberg School of Public Health
|For his influential work addressing high-dimensional data; for development of empirical tools for data science as a science with applications to meta-research, reproducibility, and replicability; for scaling (bio)statistics-centered data science education to millions of people worldwide; and for leveraging data science tools, educational technologies, and community partnerships to create economic opportunities in under-served communities.
University of Washington
|For bridging the gap between the questions that scientists are asking about their data and the statistical methods that are available to provide insightful answers, especially in the context of biomedical research; for developing flexible and interpretable approaches for modeling large-scale and high-dimensional data; and for the significant elevation of statistical science via successful translation of statistical ideas to a broad audience.