IMS Announces 2009 Fellows
After reviewing all the nominations submitted this year, 17 IMS members have been selected for Fellowship. Approximately 5% of the current IMS membership has earned the status of fellowship. This year’s Fellows are:
- Jianwen Cai, University of North Carolina at Chapel Hill: For outstanding contributions in multivariate survival analysis and analysis of correlated survival data, outstanding teaching and service to the profession.
- Song Xi Chen, Iowa State University and Peking University: For fundamental and important contributions to empirical likelihood and nonparametric function estimation, and for development of novel statistical techniques for estimation of animal abundance, analysis of financial data and multiple system surveys.
- Gauri Sankar Datta, University of Georgia: For outstanding research in Bayesian statistics, survey sampling and asymptotic theory, for elegant real-life applications of the theory developed, and for excellent dissemination of ideas through an authoritative research monograph.
- David Draper, University of California at Santa Cruz: For seminal contributions to Bayesian hierarchical modeling, nonparametric methods, Markov chain Monte Carlo, quality assessment in health and education, and stochastic optimization, for extraordinary service to the profession and for broad and high-impact contributions to statistics education.
- Jean-Pierre Fouque, University of California at Santa Barbara: For contributions to interacting particle systems, waves in random media, and financial mathematics; and for excellent mentoring of graduate students and postdocs.
- Wing Kam Fung, The University of Hong Kong: For significant contributions to robust statistics and forensic statistics, and for leadership in Asia for statistical research and education.
- Joseph Glaz, University of Connecticut: For international leadership in the field of scan statistics, for significant contributions in multivariate dependence, sequential analysis and applied probability, and for excellent editorial service.
- Feifang Hu, University of Virginia: For innovative and significant research on adaptive designs and resampling methods, and for strong commitment to the profession through collaboration, student mentoring and professional service.
- Adam Jakubowski, Nicolaus Copernicus University: For contributions to limit theorems for dependent sequences, functional limit theorems of stochastic integrals, and for achievements in organizational work in the probability community.
- John E. Kolassa, Rutgers – The State University of New Jersey: For outstanding contributions to saddlepoint approximations and Edgeworth expansions and their applications.
- Runze Li, The Pennsylvania State University at University Park: For fundamental contributions to variable selection in high-dimensional modeling, for significant contributions to semiparametric regression for longitudinal data, and for excellent editorial service.
- Hannu Oja, University of Tampere: For significant and seminal contributions to the development of robust affine equivariant and invariant multivariate methods and for leadership in statistics research.
- Liang Peng, Georgia Institute of Technology: For innovative contributions to extreme value theory and heavy tailed data analysis, and for dedicated editorial service.
- Michael Sørensen, University of Copenhagen: For fundamental research on the theory of inference for stochastic processes, for incisive applied research in biology, finance, and geophysics, and for outstanding national and international research leadership.
- Boxin Tang, Simon Fraser University: For seminal contributions to construction and optimality theory of combinatorial design and for dedication in editorial service.
- Marina Vannucci, Rice University: For fundamental contributions to the theory and practice of Bayesian methods for variable selection, and of wavelet-based modeling, and for mentorship of young researchers.
- Rainer von Sachs, Université catholique de Louvain: For outstanding contributions in time series analysis and nonparametric smoothing of correlated data using wavelet and time-frequency localization methods.
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