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国际数理统计学会Institute of Mathematical Statistics,简称:IMS,创立于1933年,是最权威的全球性统计与概率国际学术组织之一。 学会总部设在美国。 学会着重发展和推广统计与概率的理论及应用,现有来自世界各国的会员约4000人。在2015年新增选的17位会士名单中,5位华人教授当选,包括Yaozhong Hu,Ming Yuan,Tong Zhang,Ji Zhu,Hui Zou。http://bulletin.imstat.org/2015/05/ims-fellows-2015/
所有的IMS会士名单
http://www.imstat.org/awards/honored_fellows.htm
We announce the class of new IMS Fellows for 2015, who will be presented at the IMS Presidential Address and Awards session at JSM in Seattle. Congratulations, Fellows!
2015年新当选的名单
Sudipto Banerjee, University of California, Los Angeles
For outstanding methodological contributions to the field of spatial and spatio-temporal statistics and for his vibrant interest in challenging environmental and biomedical applications.
Noureddine El Karoui, University of California, Berkeley
For fundamental contributions to statistical methods in high dimension, especially in the study of high-dimensional sample covariance matrices with sparse entries
Peter Guttorp, University of Washington
For influential contributions to inference for stochastic processes, spatial statistics and time series having a profound impact in environmental science and biology; and for leadership in strengthening statistics’ link to the atmospheric and climate sciences.
Ben Hambly, University of Oxford
For fundamental contributions to probability theory, and in particular to our understanding of random motions in random graphs and random environments.
Yaozhong Hu, University of Kansas
For fundamental research in stochastic calculus for fractional Brownian motion; and for influential work in stochastic partial differential equations.
Davar Khoshnevisan, University of Utah
For outstanding work in the theory of stochastic processes, in particular: geometric and asymptotic properties of random fields, and chaotic behavior of stochastic partial differential equations.
Axel Munk, Georg August Univ Göttingen & Max Planck Institute for Biophysical Chemistry
For ground-breaking contributions to change-point problems, fundamental research in inverse problems and its applications to biophysics, influential work on data analysis on manifolds and fingerprints, and leadership in German statistical community.
Douglas William Nychka, National Center for Atmospheric Research
For outstanding theoretical contributions to nonparametric regression and the statistical analysis of dynamical systems; for development of widely used statistical software; for leadership in statistical climatology research and advocacy for statistics.
Igor Prünster, Università di Torino & Collegio Carlo Alberto
For groundbreaking research on discrete random measures and their applications, and for outstanding service to the profession.
Gesine Reinert, University of Oxford
For fundamental contributions to probability and asymptotic statistics, and for and deep and important applications in the life sciences.
Sylvia Richardson, University of Cambridge and MRC Biostatistics Unit
For influential research in spatial statistics, hierarchical modeling, mixture models; for applications in biomedical science, epidemiology and genomics, and for service to the profession.
Judith Rousseau, Université Paris Dauphine
For fundamental contributions to Bayesian statistics, including Bernstein—von Mises theorems and Bayesian nonparametrics, and for outstanding service to the community.
Laurent Younes, Johns Hopkins University,
For fundamental contributions to the mathematical and statistical foundations of shape and image analysis; and for new methodology implemented in large and important medical imaging studies.
Ming Yuan, University of Wisconsin at Madison
For fundamental contributions to nonparametric function estimation and high-dimensional statistical inference.
Tong Zhang, Rutgers University
For influential contributions to statistical theory and methodology, especially in statistical machine learning and high dimensional data.
Ji Zhu, University of Michigan–Ann Arbor
For outstanding research accomplishments on statistical learning.
Hui Zou, University of Minnesota
For fundamental contributions to high-dimensional statistics, machine learning and statistical computing and for excellent editorial service.
见http://www.mathsccnu.com/forum.php?mod=viewthread&tid=1823
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