معرفی
Jing Lei is a Professor in the Department of Statistics & Data Science at Carnegie Mellon University, affiliated with the Dietrich College of Humanities and Social Sciences. His research focuses on statistical theory and methodology for high-dimensional, complex data with applications in machine learning, genomics, and network analysis.
- Ph.D., Department of Statistics, UC Berkeley (2010)
- Prior: Statistician at Google (2010-2011), Visiting Research Scientist at CMU (2011)
Research interests include:
- Statistical Machine Learning: Conformal Prediction, Cross-Validation, Spectral Clustering
- High-Dimensional Statistics: Sparse PCA, Dimension Reduction, Network Estimation
- Nonparametric Methods: Smoothing Techniques, Functional Data Analysis
- Data Privacy: Differentially Private Estimators, Distribution-Free Inference
- Applications: Single-cell RNA-seq, Multi-omics Data, Astronomy
Recent work involves minimax optimal graphon estimation, discrete argmin inference, and adaptive conformal prediction. Key trends include network data analysis, high-dimensional hypothesis testing, and privacy-preserving statistical methods.
Scientific awards:
- Best Paper Award, AISTATS 2012
Editorial service:
- Associate Editor, Journal of the Royal Statistical Society: Series B (2024-present)
- Associate Editor, Annals of Statistics (2019-present)
- Associate Editor, Journal of the American Statistical Association (2020-present)
Currently teaching 36710 Advanced Probability and Statistics (Fall 2024). Affiliated with CMU research labs including CSAFE and DELPHI.
