Yang Feng

Professor of Biostatistics

Professional overview

Yang Feng is a Professor and Ph.D. Program Director of Biostatistics in the School of Global Public Health and an affiliate faculty in the Center for Data Science at New York University. He obtained his Ph.D. in Operations Research at Princeton University in 2010.

Feng's research interests encompass the theoretical and methodological aspects of machine learning, high-dimensional statistics, social network models, and nonparametric statistics, leading to a wealth of practical applications, including Alzheimer's disease, cancer classification, and electronic health records. His research has been funded by multiple grants from the National Institutes of Health (NIH) and the National Science Foundation (NSF), notably the NSF CAREER Award.

He is currently an Associate Editor for the Journal of the American Statistical Association (JASA), the Journal of Business & Economic Statistics (JBES), Journal of Computational & Graphical Statistics (JCGS), and the Annals of Applied Statistics (AoAS). His professional recognitions include being named a fellow of the American Statistical Association (ASA) and the Institute of Mathematical Statistics (IMS), as well as an elected member of the International Statistical Institute (ISI).

Please visit Dr. Yang Feng's website and Google Scholar page from more information.

Education

B.S. in Mathematics, University of Science and Technology of China, Hefei, China

Ph.D. in Operations Research, Princeton University, Princeton, NJ

Areas of research and study

Bioinformatics

Biostatistics

High-dimensional data analysis/integration

Machine learning

Modeling Social and Behavioral Dynamics

Nonparametric statistics

Publications

Publications

Accounting for incomplete testing in the estimation of epidemic parameters

Feng, Y., Betensky, R. A., & Feng, Y. (n.d.).

Publication year

2020

Journal title

International Journal of Epidemiology

Abstract

Abstract

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Accounting for incomplete testing in the estimation of epidemic parameters

Betensky, R. A., & Feng, Y. (n.d.).

Publication year

2020

Journal title

International Journal of Epidemiology

Volume

49

Issue

5

Page(s)

1419-1426

10.1093/ije/dyaa116

Abstract

Abstract

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Nested Model Averaging on Solution Path for High-dimensional Linear Regression

Feng, Y., Feng, Y., & Liu, Q. (n.d.).

Publication year

2020

Journal title

Stat

Abstract

Abstract

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Neyman-Pearson classification: parametrics and sample size requirement

Feng, Y., Tong, X., Xia, L., Wang, J., & Feng, Y. (n.d.).

Publication year

2020

Journal title

Journal of Machine Learning Research

Abstract

Abstract

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On the estimation of correlation in a binary sequence model

Feng, Y., Weng, H., & Feng, Y. (n.d.).

Publication year

2020

Journal title

Journal of Statistical Planning and Inference

Volume

207

Page(s)

123--137

Abstract

Abstract

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On the sparsity of Mallows model averaging estimator

Feng, Y., Feng, Y., Liu, Q., & Okui, R. (n.d.).

Publication year

2020

Journal title

Economics Letters

Volume

187

Page(s)

108916

Abstract

Abstract

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A Kronecker Product Model for Repeated Pattern Detection on 2D Urban Images

Liu, J., Psarakis, E. Z., Feng, Y., & Stamos, I. (n.d.).

Publication year

2019

Journal title

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volume

41

Issue

9

Page(s)

2266-2272

10.1109/TPAMI.2018.2858795

Abstract

Abstract

Repeated patterns (such as windows, balconies, and doors) are prominent and significant features in urban scenes. Therefore, detection of these repeated patterns becomes very important for city scene analysis. This paper attacks the problem of repeated pattern detection in a precise, efficient and automatic way, by combining traditional feature extraction with a Kronecker product based low-rank model. We introduced novel algorithms that extract repeated patterns from rectified images with solid theoretical support. Our method is tailored for 2D images of building façades and tested on a large set of façade images.

Likelihood adaptively modified penalties

Feng, Y., Feng, Y., Li, T., & Ying, Z. (n.d.).

Publication year

2019

Journal title

Applied Stochastic Models in Business and Industry

Volume

35

Issue

2

Page(s)

330--353

Abstract

Abstract

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Regularization after retention in ultrahigh dimensional linear regression models

Feng, Y., Weng, H., Feng, Y., & Qiao, X. (n.d.).

Publication year

2019

Journal title

Statistica Sinica

Abstract

Abstract

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The restricted consistency property of leave-$n_v$-out cross-validation for high-dimensional variable selection

Feng, Y., Feng, Y., & Yu, Y. i. (n.d.).

Publication year

2019

Journal title

Statistica Sinica

Volume

29

Page(s)

1607--1630

Abstract

Abstract

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