Yang Feng

708 Broadway, Room 415

Department of Biostatistics

New York University

New York, NY 10003

Email: yf31@nyu.edu

Academic Appointments

Professor and Ph.D. Program Director, Department of Biostatistics, School of Global Public Health, New York University
Affiliate Faculty, Center for Data Science, New York University
Affiliate Faculty, PRIISM, New York University

Research Interests

Machine Learning

Transfer learning
Multi-task learning
Federated learning
Neyman-Pearson classification
Causal inference
Deep learning

High-Dimensional Statistics

Variable selection
Variable screening
Gaussian graphical models

Network Models

Community detection
Network embedding

Applications

Electronic health records
Genomics
Epidemiology
Neuroscience
Social networks
Computer vision

📄 Google Scholar Profile (View by Year)

📄 Short CV (PDF)

Editorial Activities

Associate Editor, Journal of the American Statistical Association (JASA)
Associate Editor, Annals of Applied Statistics (AOAS)
Associate Editor, Journal of Business & Economic Statistics (JBES)
Associate Editor, Journal of Computational & Graphical Statistics (JCGS)

Selected Honors

Fellow, American Statistical Association (ASA)
Fellow, Institute of Mathematical Statistics (IMS)
Elected Member, International Statistical Institute (ISI)

Research Support

NSF Grant DMS-2324489: Collaborative Research: New Theory and Methods for High-Dimensional Multi-Task and Transfer Learning Inference

news

Jun 18, 2025	Paper accepted at JASA
Mar 15, 2025	Paper accepted at AoS
Nov 27, 2024	A draft of my book on R Programming is now available

latest posts

Dec 04, 2024	My Spanish Learning Journey

selected publications

Semiparametric Modeling and Analysis for Longitudinal Network Data

Yinqiu He, Jiajin Sun, Yuang Tian, Zhiliang Ying, and Yang Feng

Annals of Statistics, 2025

arXiv Bib Code

@article{he2023semiparametric,
  author = {He, Yinqiu and Sun, Jiajin and Tian, Yuang and Ying, Zhiliang and Feng, Yang},
  journal = {Annals of Statistics},
  title = {Semiparametric Modeling and Analysis for Longitudinal Network Data},
  year = {2025},
}

Design-Based Causal Inference with Missing Outcomes: Missingness Mechanisms, Imputation-Assisted Randomization Tests, and Covariate Adjustment

Siyu Heng, Jiawei Zhang, and Yang Feng

Journal of the American Statistical Association, 2025

DOI arXiv Bib Code

@article{heng2023design,
  author = {Heng, Siyu and Zhang, Jiawei and Feng, Yang},
  journal = {Journal of the American Statistical Association},
  title = {Design-Based Causal Inference with Missing Outcomes: Missingness Mechanisms, Imputation-Assisted Randomization Tests, and Covariate Adjustment},
  year = {2025},
  doi = {10.1080/01621459.2025.2516204},
}

Neyman-pearson multi-class classification via cost-sensitive learning

Ye Tian, and Yang Feng

Journal of the American Statistical Association, 2024

DOI Bib Code

@article{tian2024neyman,
  author = {Tian, Ye and Feng, Yang},
  journal = {Journal of the American Statistical Association},
  title = {Neyman-pearson multi-class classification via cost-sensitive learning},
  year = {2024},
  number = {just-accepted},
  pages = {1--23},
  publisher = {Taylor \& Francis},
  arixv = {2111.04597},
  doi = {10.1080/01621459.2024.2402567},
}

Towards the Theory of Unsupervised Federated Learning: Non-asymptotic Analysis of Federated EM Algorithms

Ye Tian, Haolei Weng, and Yang Feng

In Forty-first International Conference on Machine Learning, 2024

arXiv Bib

@inproceedings{tian2024towards,
  author = {Tian, Ye and Weng, Haolei and Feng, Yang},
  booktitle = {Forty-first International Conference on Machine Learning},
  title = {Towards the Theory of Unsupervised Federated Learning: Non-asymptotic Analysis of Federated EM Algorithms},
  year = {2024},
}

Transfer learning under high-dimensional generalized linear models

Ye Tian, and Yang Feng

Journal of the American Statistical Association, 2023

Bib PDF Code

@article{tian2023transfer,
  author = {Tian, Ye and Feng, Yang},
  journal = {Journal of the American Statistical Association},
  title = {Transfer learning under high-dimensional generalized linear models},
  year = {2023},
  number = {544},
  pages = {2684--2697},
  volume = {118},
  publisher = {Taylor \& Francis},
}

DDAC-SpAM: A Distributed Algorithm for Fitting High-dimensional Sparse Additive Models with Feature Division and Decorrelation

Yifan He, Ruiyang Wu, Yong Zhou, and Yang Feng

Journal of the American Statistical Association, 2023

Bib PDF

@article{he2023ddac,
  author = {He, Yifan and Wu, Ruiyang and Zhou, Yong and Feng, Yang},
  journal = {Journal of the American Statistical Association},
  title = {DDAC-SpAM: A Distributed Algorithm for Fitting High-dimensional Sparse Additive Models with Feature Division and Decorrelation},
  year = {2023},
  pages = {1--12},
  publisher = {Taylor \& Francis},
}

PCABM: Pairwise Covariates-Adjusted Block Model for Community Detection

Sihan Huang, Jiajin Sun, and Yang Feng

Journal of the American Statistical Association, 2023

Bib PDF Code

@article{huang2023pcabm,
  author = {Huang, Sihan and Sun, Jiajin and Feng, Yang},
  journal = {Journal of the American Statistical Association},
  title = {PCABM: Pairwise Covariates-Adjusted Block Model for Community Detection},
  year = {2023},
  pages = {1--13},
  publisher = {Taylor \& Francis},
}

Testing community structure for hypergraphs

Mingao Yuan, Ruiqi Liu, Yang Feng, and Zuofeng Shang

Annals of Statistics, 2022

Bib PDF

@article{yuan2022testing,
  author = {Yuan, Mingao and Liu, Ruiqi and Feng, Yang and Shang, Zuofeng},
  journal = {Annals of Statistics},
  title = {Testing community structure for hypergraphs},
  year = {2022},
  number = {1},
  pages = {147--169},
  volume = {50},
  publisher = {Institute of Mathematical Statistics},
}

Large-scale model selection in misspecified generalized linear models

Emre Demirkaya, Yang Feng, Pallavi Basu, and Jinchi Lv

Biometrika, 2022

Bib PDF

@article{demirkaya2022large,
  author = {Demirkaya, Emre and Feng, Yang and Basu, Pallavi and Lv, Jinchi},
  journal = {Biometrika},
  title = {Large-scale model selection in misspecified generalized linear models},
  year = {2022},
  number = {1},
  pages = {123--136},
  volume = {109},
  publisher = {Oxford University Press},
}

RaSE: Random Subspace Ensemble Classification

Ye Tian, and Yang Feng

Journal of Machine Learning Research, 2021

Bib PDF Code

@article{tian2021raseclassification,
  author = {Tian, Ye and Feng, Yang},
  journal = {Journal of Machine Learning Research},
  title = {RaSE: Random Subspace Ensemble Classification},
  year = {2021},
  date-modified = {2024-11-12 13:09:11 -0500},
  read = {0},
}

The Interplay of Demographic Variables and Social Distancing Scores in Deep Prediction of US COVID-19 Cases

Francesca Tang, Yang Feng, Hamza Chiheb, and Jianqing Fan

Journal of the American Statistical Association, 2021

Bib PDF

@article{tang2021interplay,
  author = {Tang, Francesca and Feng, Yang and Chiheb, Hamza and Fan, Jianqing},
  journal = {Journal of the American Statistical Association},
  title = {The Interplay of Demographic Variables and Social Distancing Scores in Deep Prediction of US COVID-19 Cases},
  year = {2021},
}

RaSE: A Variable Screening Framework via Random Subspace Ensembles

Ye Tian, and Yang Feng

Journal of American Statistical Association, 2021

Bib PDF Code

@article{tian2021rasescreening,
  author = {Tian, Ye and Feng, Yang},
  journal = {Journal of American Statistical Association},
  title = {RaSE: A Variable Screening Framework via Random Subspace Ensembles},
  year = {2021},
  date-modified = {2024-11-12 13:08:50 -0500},
}

A Projection Based Conditional Dependence Measure with Applications to High-dimensional Undirected Graphical Models

Jianqing Fan, Yang Feng, and Lucy Xia

Journal of Econometrics, 2020

Bib PDF Code

@article{fan2020projection,
  author = {Fan, Jianqing and Feng, Yang and Xia, Lucy},
  journal = {Journal of Econometrics},
  title = {A Projection Based Conditional Dependence Measure with Applications to High-dimensional Undirected Graphical Models},
  year = {2020},
}

Neyman-Pearson classification: parametrics and sample size requirement

Xin Tong, Lucy Xia, Jiacheng Wang, and Yang Feng

Journal of Machine Learning Research, 2020

Bib PDF

@article{tong2020neyman,
  author = {Tong, Xin and Xia, Lucy and Wang, Jiacheng and Feng, Yang},
  journal = {Journal of Machine Learning Research},
  title = {Neyman-Pearson classification: parametrics and sample size requirement},
  year = {2020},
}

A kronecker product model for repeated pattern detection on 2d urban images

Juan Liu, Emmanouil Z Psarakis, Yang Feng, and Ioannis Stamos

IEEE transactions on pattern analysis and machine intelligence, 2019

Bib PDF Supp

@article{liu2019kronecker,
  author = {Liu, Juan and Psarakis, Emmanouil Z and Feng, Yang and Stamos, Ioannis},
  journal = {IEEE transactions on pattern analysis and machine intelligence},
  title = {A kronecker product model for repeated pattern detection on 2d urban images},
  year = {2019},
  number = {9},
  pages = {2266--2272},
  volume = {41},
  publisher = {IEEE},
}

Neyman-Pearson classification algorithms and NP receiver operating characteristics

Xin Tong, Yang Feng, and Jingyi Jessica Li

Science Advances, 2018

Bib PDF Code

@article{tong2018neyman,
  author = {Tong, Xin and Feng, Yang and Li, Jingyi Jessica},
  journal = {Science Advances},
  title = {Neyman-Pearson classification algorithms and NP receiver operating characteristics},
  year = {2018},
  number = {2},
  pages = {eaao1659},
  volume = {4},
  publisher = {American Association for the Advancement of Science},
}

Model selection for high-dimensional quadratic regression via regularization

Ning Hao, Yang Feng, and Hao Helen Zhang

Journal of the American Statistical Association, 2018

Bib PDF Code

@article{hao2018model,
  author = {Hao, Ning and Feng, Yang and Zhang, Hao Helen},
  journal = {Journal of the American Statistical Association},
  title = {Model selection for high-dimensional quadratic regression via regularization},
  year = {2018},
  number = {522},
  pages = {615--625},
  volume = {113},
  publisher = {Taylor \& Francis},
}

Feature Augmentation via Nonparametrics and Selection (FANS) in high-dimensional classification

Jianqing Fan, Yang Feng, Jiancheng Jiang, and Xin Tong

Journal of the American Statistical Association, 2016

Bib PDF

@article{fan2016feature,
  author = {Fan, Jianqing and Feng, Yang and Jiang, Jiancheng and Tong, Xin},
  journal = {Journal of the American Statistical Association},
  title = {Feature Augmentation via Nonparametrics and Selection (FANS) in high-dimensional classification},
  year = {2016},
  number = {513},
  pages = {275--287},
  volume = {111},
  publisher = {Taylor \& Francis},
}

Neyman-Pearson classification under high-dimensional settings

Anqi Zhao, Yang Feng, Lie Wang, and Xin Tong

Journal of Machine Learning Research, 2016

Bib PDF

@article{zhao2016neyman,
  author = {Zhao, Anqi and Feng, Yang and Wang, Lie and Tong, Xin},
  journal = {Journal of Machine Learning Research},
  title = {Neyman-Pearson classification under high-dimensional settings},
  year = {2016},
  number = {212},
  pages = {1--39},
  volume = {17},
}

A road to classification in high dimensional space: the regularized optimal affine discriminant

Jianqing Fan, Yang Feng, and Xin Tong

Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2012

Bib PDF

@article{fan2012road,
  author = {Fan, Jianqing and Feng, Yang and Tong, Xin},
  journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
  title = {A road to classification in high dimensional space: the regularized optimal affine discriminant},
  year = {2012},
  number = {4},
  pages = {745--771},
  volume = {74},
  publisher = {Wiley Online Library},
}

Nonparametric independence screening in sparse ultra-high-dimensional additive models

Jianqing Fan, Yang Feng, and Rui Song

Journal of the American Statistical Association, 2011

Bib PDF

@article{fan2011nonparametric,
  author = {Fan, Jianqing and Feng, Yang and Song, Rui},
  journal = {Journal of the American Statistical Association},
  title = {Nonparametric independence screening in sparse ultra-high-dimensional additive models},
  year = {2011},
  pages = {544--557},
  volume = {106},
  publisher = {Taylor \& Francis},
}

Nonparametric estimation of genewise variance for microarray data

Jianqing Fan, Yang Feng, and Yue S Niu

Annals of Statistics, 2010

Bib PDF

@article{fan2010nonparametric,
  author = {Fan, Jianqing and Feng, Yang and Niu, Yue S},
  journal = {Annals of Statistics},
  title = {Nonparametric estimation of genewise variance for microarray data},
  year = {2010},
  number = {5},
  pages = {2723},
  volume = {38},
  publisher = {NIH Public Access},
}

Local quasi-likelihood with a parametric guide

Jianqing Fan, Yichao Wu, and Yang Feng

Annals of Statistics, 2009

Bib PDF

@article{fan2009local,
  author = {Fan, Jianqing and Wu, Yichao and Feng, Yang},
  journal = {Annals of Statistics},
  title = {Local quasi-likelihood with a parametric guide},
  year = {2009},
  number = {6B},
  pages = {4153},
  volume = {37},
  publisher = {NIH Public Access},
}