Scholar
Chenlei Leng (冷琛雷)
Google Scholar ID: rsT2stMAAAAJ
Chair Professor of Statistics and Machine Learning, Hong Kong Polytechnic University
Citations & Impact
Citations
2,794
H-index
28
i10-index
53
Publications
20
Co-authors
6
list available
Publications
Browse publications on Google Scholar (top-right) ↗
Resume (English only)
Academic Achievements
- Fellow of the Institute of Mathematical Statistics (IMS) and Elected Member of the International Statistical Institute (ISI). Former Humboldt Fellow and Inaugural Turing Fellow at the Alan Turing Institute. Previous leadership roles include Chair of the Research Section of the Royal Statistical Society and Co-Director of the Oxford–Warwick Statistics Centre for Doctoral Training. Currently serves as Associate Editor for the Journal of Computational and Graphical Statistics and Computational Statistics & Data Analysis, and previously served on the editorial board of the Journal of the Royal Statistical Society: Series B.
Research Experience
- Chair Professor of Statistics and Machine Learning in the Department of Applied Mathematics (AMA) at the Hong Kong Polytechnic University (PolyU). Has held academic positions at leading institutions worldwide, including the National University of Singapore (NUS), Peking University (PKU), the University of Munich (LMU), and the University of Warwick.
Education
- Bachelor's degree in Mathematics from the University of Science and Technology of China (USTC); Ph.D. in Statistics from the University of Wisconsin–Madison.
Background
- A statistician and machine learning researcher working at the intersection of statistics, machine learning, and data science. Research focuses on high-dimensional inference, network and graph-based models, structured and correlated data, nonparametric and computational methods, and statistical foundations of AI.
Miscellany
- Actively recruiting PhD students, postdoctoral fellows, and summer interns who possess strong mathematical and statistical foundations and a genuine curiosity for theory-driven advances in modern data science.
