The designation of IMS Fellow has been a significant honor for 90 years. Each Fellow has demonstrated distinction in research in statistics or probability or has demonstrated leadership that has profoundly influenced the field.

Francesco Bartolucci

University of Perugia

For outstanding contributions to theory, computing, and application of latent variable modeling, with creative methodological work for categorical and longitudinal data analyses.

Nathanaël Berestycki

Universität Wien 

For profound contributions to probability theory and mathematical physics, particularly to the areas of Liouville quantum gravity, coalescent theory, and Gaussian multiplicative chaos.

Jose Blanchet

Stanford University

For outstanding contributions to the IMS stochastics and applied probability communities, for impact on disciplines beyond the IMS community, including the larger Monte Carlo, operations research, and machine learning communities.

Jelena Bradic

Cornell University 

For innovative contributions in causal inference, robust machine learning methods, double robustness, for development of inferential methods that do not rely on sparsity in high-dimensional settings, and for exceptional service to the statistical community.

Tianxi Cai

Harvard University 

For pioneering contributions to translational statistics and developing end-to-end informatics tools for multi-institutional Electronic Health Records (EHR) data, enabling scalable and reliable research. For fundamental innovations in survival analysis, predictive modeling, precision medicine, and high-dimensional statistical learning. For groundbreaking advancements in integrating statistical and machine learning methods for robust EHR analysis. For exceptional mentorship and professional service to the field.

Jian Ding

Peking University 

For profound and outstanding contributions to the probabilistic theory of disordered systems.

Philip A. Ernst

Imperial College London 

For groundbreaking contributions to exact distribution theory, mathematical finance, optimal stopping, queueing systems, and stochastic control.

Peter Daniel Grünwald

CWI 

For outstanding foundational contributions to statistics and statistical learning theory, such as e-values, Bayesian inference under misspecification, and minimum description length learning.

Nicholas J. Horton

Amherst College 

For exceptional scholarly contributions to undergraduate education of statistics and data science, and for highly influential leadership and services in the statistics profession.

Chiung-Yu Huang

University of California, San Francisco 

For outstanding contributions to statistical methodology, especially in survival analysis, longitudinal data analysis, recurrent event analysis, panel count data analysis, biased sampling, and the evaluation of vaccine efficacy trials; for excellent service to the profession through mentoring and editorial efforts.

Ching-Kang Ing

National Tsing Hua University 

For fundamental and pioneering contributions to model selection and prediction in time series analysis, for significant contributions to nonlinear least squares estimation of stochastic models, and for exemplified services to statistical education and profession.

Chuanhai Liu

Purdue University 

For highly original contributions to the foundation of statistical inference and computational statistics.

Po-Ling Loh

University of Cambridge 

For fundamental contributions to high-dimensional statistics and machine learning, in particular, the study of non-convex penalized estimators, robust statistics, network inference and differential privacy, and for substantial contributions to the profession through work with statistical societies and editorial service.

Qi Long

University of Pennsylvania

For distinguished contributions to advancing statistics and data science; for outstanding leadership in the impactful application of statistics in biomedicine.

Elchanan Mossel

MIT 

For deep, seminal contributions to probability theory and combinatorial statistics, with wide-ranging applications in computational biology, network science, mathematical economics, and the theory of computing.

Bhramar Mukherjee

Yale School of Public Health 

For influential contributions to the statistics of public health, particularly in developing empirical Bayes methods for case-control studies, advancing gene-environment interaction analysis, mediation analysis, and infectious disease research, for dedicated mentorship of students and early career researchers, and for exemplary leadership and service to the profession.

Roberto Imbuzeiro Oliveira

Instituto de Matemática Pura e Aplicada (IMPA) 

For being a leading researcher in probability and mathematical statistics with a rarely seen breadth of interests and expertise.

Soumik Pal

University of Washington, Seattle 

For significant contributions to the mathematics and applications of probability, the educational environment, and to the mission of the Institute of Mathematical Statistics.

Aaditya Kumar Ramdas

Carnegie Mellon University 

For pioneering the new subfield of game-theoretic statistics based on e-values, confidence sequences and betting, and for fundamental contributions to post-selection inference and multiple testing, conformal prediction and statistical machine learning.

James M. Robins

Harvard TH Chan School of Public Health 

For outstanding and sustained contributions to statistical theory and methods for causal inference and semiparametrics, and for dedication to mentoring young statisticians.

Andrea Rotnitzky

University of Washington, Seattle 

For groundbreaking contributions to semiparametric theory for causal inference and missing data analysis, the development of doubly robust estimation, the integration of semiparametric efficiency theory with causal graphical models.

Rajen D. Shah

University of Cambridge 

For developing highly innovative and important statistical methodology and theory for conditional independence testing, variable selection, covariance matrix estimation, goodness-of-fit testing, and for exemplary service to the profession.

Allan Murray Sly

Princeton University 

For being one of the best probabilists in the world with astounding impact on the field – having completely settled formidable problems that had challenged, and often baffled, probabilists, statisticians and computer scientists for decades.

Weijie Su

University of Pennsylvania 

For groundbreaking advancements in statistical viewpoints on privacy-preserving data analysis; for novel applications of shape-restricted regression to enhance peer review; for fundamental progress in understanding gradient-based optimization methods; and for significant contributions to the theoretical underpinnings of deep learning.