Feng’s research interests encompass the theoretical and methodological aspects of machine learning, high-dimensional statistics, network models, and nonparametric statistics, leading to a wealth of practical applications. He has published over 60 peer-reviewed articles with over 3,600 Google Scholar Citations.
He is currently an associate editor for the Annals of Applied Statistics, Journal of American Statistical Association, Journal of Business & Economic Statistics, and Statistica Sinica. His research has been generously supported by multiple grants from the National Science Foundation (NSF) and the National Institutes of Health (NIH). He is a fellow of the American Statistical Association (ASA), the Institute of Mathematical Statistics (IMS) and an elected member of the International Statistical Institute (ISI).
Dr. Feng was elected as a fellow of the Institute of Mathematical Statistics (IMS) in 2023. “For outstanding contributions to high-dimensional statistics, nonparametric statistics, social network analysis, and statistical machine learning; for statistical software development; and for dedicated service to the profession.”
Dr. Feng was elected as a fellow of the American Statistical Association (ASA) in 2022. “For development of effective, practical, and efficient statistical methods that are backed by theory and are relevant and accessible to practitioners; for wide dissemination of methods in publicly available software; and for outstanding teaching.”
He has published papers in Annals of Statistics, Annals of Applied Statistics, Biometrika, IEEE Transactions on Pattern Analysis and Machine Intelligence, Journal of Royal Statistical Society Series B, Journal of the American Statistical Association, Journal of Machine Learning Research, Science Advances, Journal of Econometrics, Journal of Business & Economic Statistics, etc.
He is currently an associate editor for
His research is partially supported by
NIH 1R21AG074205-01: Multiclass classification under prioritized error control and specific error costs with applications to dementia classification
NSF Grant DMS-2324489: Collaborative Research: New Theory and Methods for High-Dimensional Multi-Task and Transfer Learning Inference
Feng Lab is continuously looking for talents at undergraduate, master and Ph.D. level. If you are interested, please submit an application at https://bit.ly/2KMEflH.
PhD in Operations Research, 2010
BS in Mathematics, 2006
University of Science and Technology of China (USTC)