Congratulations to the 2026 Class of IMS Fellows!

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.

Alexandre Belloni, University of Chicago

For contributions to data science with the striking combination of breadth and technical depth, including high-dimensional statistics, econometrics and operations research; and for influential leadership in data science.

Bhaswar Bikram Bhattacharya, University of Pennsylvania

For important contributions to probability theory, computational geometry, random graph theory, combinatorial optimization, nonparametric statistics and statistical methods for network data; for dedicating mentoring and service to the profession.

Vivek Shripad Borkar, Indian Institute of Technology, Bombay

For significant fundamental contributions to stochastic approximation and control theory.

Yong Chen, University of Pennsylvania

For outstanding methodological contributions and impactful biomedical applications across multiple areas, including data integration, privacy-preserving methods, and inference under non-standard conditions; and for distinguished mentorship of trainees.

Yuxin Chen, University of Pennsylvania

For seminal contributions to nonconvex statistical estimation, statistical analysis of spectral methods, and statistical foundations of modern artificial intelligence.

Peng Ding, University of California Berkeley

For outstanding contributions to causal inference theory and methodology; and for dedicated service to the profession through editorial leadership and mentorship, with broad impact across epidemiology, economics, and the social sciences.

Kim-Anh Do, University of Texas MD Anderson Cancer Center

For sustained, impactful contributions to computational statistics and cutting-edge translational data science methodology focusing on integrative genomics and proteomics; for outstanding records of mentoring women biostatisticians and service to the profession.

Adityanand Guntuboyina, University of California Berkeley

For significant fundamental contributions to the theory of nonparametric and high-dimensional inference, including shape-constrained estimation and nonparametric empirical Bayes methodology.

Bénédicte Haas, Université Sorbonne Paris Nord

For seminal contributions to the study of fragmentation processes, random trees and their scaling limits.

Hsin-Cheng Huang, Academia Sinica, Taiwan

For significant fundamental contributions to spatial and spatiotemporal statistics; and for distinguished service to the statistics profession.

Mia Hubert, KU Leuven

For outstanding and highly influential contributions to robust multivariate methods, statistical depth, chemometrics, and clustering.

Karen Kafadar, University of Virginia

For significant fundamental work on exploratory data analysis, graphical, robust, and spatial statistics; and for important contributions to the fields of cancer research, forensic, genome sciences, and physics.

Jian Kang, University of Michigan

For influential contributions to Bayesian methods, statistical machine learning, and imaging data analysis; for outstanding leadership, mentorship, and service to the statistical community.

Mladen Kolar, University of Southern California

For important contributions to time-varying and robust graphical modeling, inference and optimization; and for excellence in mentoring of students and junior researchers.

Takashi Kumagai, Waseda University

For groundbreaking work on heat kernels, in particular of jump processes, on metric spaces and graphs.

Bo Li, Washington University in St. Louis

For significant contributions to the theory, methodology, and application of spatial and environmental statistics, particularly by bridging the fields of statistics, climate science, and human health.

Yi Li, University of Michigan

For outstanding contributions to statistics, including in survival analysis, high-dimensional inference, machine learning, spatial data analysis, and measurement error modeling; for outstanding mentoring and teaching; and for excellent service to the statistics profession.

Gabor Lugosi, ICREA & Pompeu Fabra University

For pioneering contributions to statistical learning and high-dimensional probability.

Richard Nickl, University of Cambridge

For fundamental contributions to mathematical statistics, especially to nonparametric inference and Bayesian uncertainty quantification for infinite-dimensional inverse problems; and for sustained leadership, scholarship, and service to the statistical community.

Sharon-Lise Teresa Normand, Harvard Medical School

For the careful and inventive development and application of statistical methods in health policy, especially in cardiology; and for service, mentoring, and professional leadership in medical statistics.

Daniel Remenik, Universdad de Chile

For highly influential contributions to stochastic models with non-trivial spatial interactions, especially in the understanding of the Kardar-Parisi-Zhang universality class; and for service and leadership in the Latin America probability community.

Mykhaylo Shkolnikov, Carnegie Mellon University

For impactful contributions to probability theory, with applications in financial mathematics, mean-field games, and statistical physics; for distinguished contributions to mentoring and training; and for outstanding professional service.

Bharath Sriperumbudur, Pennsylvania State University

For fundamental contributions to the theory of reproducing kernel Hilbert space embeddings of probabilities, their applications to nonparametric statistics, statistical machine learning, and optimal transport; and for dedicated service to the profession.

Junhui Wang, The Chinese University of Hong Kong

For outstanding contributions to statistical machine learning; for dedicated mentorship of students and young statisticians; and for exemplary services to the profession.

Shuheng Zhou, University of California, Riverside

For important fundamental contributions to high-dimensional statistics, differential privacy, graphical models and compressed sensing.