Congratulations to the 2019 IMS Fellows!

Edoardo M. Airoldi, For methodological contributions to modeling network data and theoretical contributions to random geometric hypergraphs.

Cristina Butucea, For deep and original contributions to non-parametric statistics, inverse problems, and quantum statistics.

Victor Chernozhukov, For path-breaking contributions to high-dimensional inference.

Jeng-Min Chiou, For contributions to methodology for clustering, classification, and prediction with functional data.

Bertrand Salem Clarke, For contributions to the theoretical justification of reference priors and on aspects of model selection involving Bayesian model averaging.

Michael Cranston, For contributions to coupling techniques resolving significant open problems for Brownian motion and questions in mathematical physics.

Robert C. Dalang, For pioneering contributions to the study of SPDEs driven by a Gaussian noise which is white in time with a spatially homogeneous covariance.

Christina Goldschmidt, For fundamental contributions to the fields of coalescence and fragmentation theory, and to continuum limits for random trees and graphs.

Yongdai Kim, For contributions to nonparametric Bayesian estimation for counting processes and high-dimensional regression.

Alois Kneip, For fundamental contributions to functional data analysis and nonparametric regression.

Shiqing Ling, For contributions to the analysis of time series with heteroscedastic and heavy-tailed noise and goodness-of-fit tests for dependent data.

Jinchi Lv, For contributions to high-dimensional statistics and causal inference.

Elisabeth S. Meckes, For contributions to Stein’s method and to random matrix theory.

Victor Panaretos, For contributions to functional data analysis and stochastic geometry, in particular to estimation of spectral density kernels for stationary time series.

Victor Pătrângenaru, For contributions to non-parametric statistics on manifolds and statistics for computer vision.

Debashis Paul, For contributions to non-parametric methods, high-dimensional multivariate analysis and random matrix theory.

Firas Rassoul-Agha, For contributions to central limit theorems and large deviations, random walks in random environments, random polymers, and related percolation models in statistical physics.

Bruno N. Rémillard, For contributions to copula modelling, to tests of independence, goodness-of-fit testing, weak convergence tools for such inference, and to quantitative finance.

Adrian Röllin, For the development of Stein’s method for multivariate cases including the unification of coupling under the name of Stein coupling.

Cynthia Rudin, For contributions to interpretable machine learning algorithms, prediction in large scale medical databases, and theoretical properties of ranking algorithms.

Xiaofeng Shao, For contributions to non-parametric statistical inference for multivariate time series, in particular to the asymptotic theory for time series analysis via moments and cumulants.

Yuedong Wang, For contributions to non-parametric regression and computational statistics, in particular smoothing spline methodology for dependent observations and applications to bioinformatics and biomedical modeling.

Christopher K. Wikle, For fundamental contributions to spatio-temporal modeling and Bayesian computation and inference, with influential applications to geophysical, ecological, and socio-demographic areas.

Hongquan Xu, For contributions to experimental design, computer experiments, and functional data analysis, in particular to nonregular fractional factorial designs and spacefilling designs.

Xiangrong Yin, For seminal work in high-dimensional data analysis and data mining, sufficient dimension reduction, and sufficient variable selection.