Rate My Professor Xinwei Deng

Deng's research focuses on the interface between experimental design and machine learning, uncertainty quantification and digital twins, modeling and analysis of high-dimensional data, covariance matrix estimation and its applications, design and analysis of computer experiments, and statistical methods for nano and emerging technologies. His contributions include over 100 peer-reviewed publications, more than 70 invited presentations at national and international conferences, and research supported by 21 grants, including six from the National Science Foundation. Key publications are "Active Learning via Sequential Design with Applications to Detection of Money Laundering" (Journal of the American Statistical Association, 2009), "A Statistical Approach to Quantifying the Elastic Deformation of Nanomaterials" (Proceedings of the National Academy of Sciences, 2009), "Modeling and Active Learning for Experiments with Quantitative-Sequence Factors" (Journal of the American Statistical Association, 2024), and "Continual Learning and Its Industrial Applications: A Selective Review" (WIREs Data Mining and Knowledge Discovery, in press). Awards include the Soren Bisgaard Award Winning Paper in Quality Engineering (2024), IISE Transactions Best Paper Award on Quality and Reliability Engineering (2017), election as Member of the International Statistical Institute (2017), and multiple top-cited article recognitions. He serves as Associate Editor for Annals of Applied Statistics (2022-present), Statistica Sinica (2020-present), International Statistical Review (2017-present), and other journals.