Xiao Wang Elected Fellow of ASA and IMS
05-19-2021
Please join us in congratulating Professor Xiao Wang on his recent elections as a Fellow of both the ASA and IMS!
The ASA website states that, “To be selected, nominees must have an established reputation and have made outstanding contributions to statistical science. The Committee on Fellows evaluates each candidate’s contributions to the advancement of statistical science and places due weight on the following:
- Published works
- Position held with employer
- ASA activities
- Membership and accomplishments in other societies
- Professional activities
From Professor Wang’s ASA fellow citation: “For outstanding contributions to statistical methodology and theory on machine learning, functional data analysis, and nonparametric statistics; for exceptional service to the profession and excellence in student mentoring.”
According to the IMS website, qualifications for fellowship include: “The candidate shall have demonstrated distinction in research in statistics or probability, by publication of independent work of merit. This qualification may be partly or wholly waived in the case of
(1) a candidate of well-established leadership whose contributions to the field of statistics or probability other than original research shall be judged of equal value; or (2) a candidate of well-established leadership in the application of statistics or probability, whose work has contributed greatly to the utility of and the appreciation of these areas.
From Professor Wang’s IMS fellow citation: “For significant contributions to nonparametric statistics, shape-restricted inference, and functional data analysis, and for dedicated professional service and students’ mentoring.”
Dr. Wang's research has contributed very significantly to machine learning, nonparametric statistics, and functional data analysis and has profound impact on engineering, astronomy, and image analysis, methods, and applications. The quality and quantity of Dr. Wang's works place him among the top statisticians with expertise on both theory and application. Dr. Wang's strong background in theoretical statistics and probability supports his expertise in weak convergence of stochastic processes, empirical processes, large sample theory, classical statistical inference, and nonparametric methods. He is well known for working both in theory and in application, and has a strong background in applied sciences. His areas of application are very diverse and range from developing methods for degradation analysis in reliability engineering, to estimation of dark matter distribution in astronomy, and to analysis of medical images in neuroimaging. In general, Dr. Wang's research is very broad and versatile, covering several areas of research. His research tackles deep methodological issues as well as very important computational, applied, and subject-matter issues.