Yang Feng

Yang Feng

Yang Feng

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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

A demonstration of the RaSEn package

Feng, Y., Tian, Y. e., & Feng, Y. (n.d.).

Publication year

2021
Abstract
Abstract
~

A flexible quasi-likelihood model for microbiome abundance count data

Feng, Y., Shi, Y., Li, H., Wang, C., Chen, J., Jiang, H., Shih, Y.-C. T., Zhang, H., Song, Y., Feng, Y., & Liu, L. (n.d.).

Publication year

2023

Journal title

Statistics in Medicine

Volume

42

Issue

25

Page(s)

4632--4643
Abstract
Abstract
~

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
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.

A likelihood-ratio type test for stochastic block models with bounded degrees

Feng, Y., Yuan, M., Feng, Y., & Shang, Z. (n.d.).

Publication year

2022

Journal title

Journal of Statistical Planning and Inference

Volume

219

Page(s)

98--119
Abstract
Abstract
~

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

Feng, Y., Fan, J., Feng, Y., & Xia, L. (n.d.).

Publication year

2020

Journal title

Journal of Econometrics
Abstract
Abstract
~

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
Abstract
Abstract
~

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
~

Analytical performance of lateral flow immunoassay for SARS-CoV-2 exposure screening on venous and capillary blood samples

Black, M. A., Shen, G., Feng, X., Garcia Beltran, W. F., Feng, Y., Vasudevaraja, V., Allison, D., Lin, L. H., Gindin, T., Astudillo, M., Yang, D., Murali, M., Iafrate, A. J., Jour, G., Cotzia, P., & Snuderl, M. (n.d.).

Publication year

2021

Journal title

Journal of Immunological Methods

Volume

489
Abstract
Abstract
Objectives: We validate the use of a lateral flow immunoassay (LFI) intended for rapid screening and qualitative detection of anti-SARS-CoV-2 IgM and IgG in serum, plasma, and whole blood, and compare results with ELISA. We also seek to establish the value of LFI testing on blood obtained from a capillary blood sample. Methods: Samples collected by venous blood draw and finger stick were obtained from patients with SARS-CoV-2 detected by RT-qPCR and control patients. Samples were tested with Biolidics 2019-nCoV IgG/IgM Detection Kit lateral flow immunoassay, and antibody calls were compared with ELISA. Results: Biolidics LFI showed clinical sensitivity of 92% with venous blood at 7 days after PCR diagnosis of SARS-CoV-2. Test specificity was 92% for IgM and 100% for IgG. There was no significant difference in detecting IgM and IgG with Biolidics LFI and ELISA at D0 and D7 (p = 1.00), except for detection of IgM at D7 (p = 0.04). Capillary blood of SARS-CoV-2 patients showed 93% sensitivity for antibody detection. Conclusions: Clinical performance of Biolidics 2019-nCoV IgG/IgM Detection Kit is comparable to ELISA and was consistent across sample types. This provides an opportunity for decentralized rapid testing and may allow point-of-care and longitudinal self-testing for the presence of anti-SARS-CoV-2 antibodies.

Association of body composition parameters measured on CT with risk of hospitalization in patients with Covid-19

Feng, Y., Chandarana, H., Pisuchpen, N., Krieger, R., Dane, B., Mikheev, A., Feng, Y., Kambadakone, A., & Rusinek, H. (n.d.).

Publication year

2021

Journal title

European Journal of Radiology

Volume

145

Page(s)

110031
Abstract
Abstract
~

Association of hyperglycemia and molecular subclass on survival in IDH-wildtype glioblastoma

Feng, Y., Liu, E. K., Vasudevaraja, V., Sviderskiy, V. O., Feng, Y., Tran, I., Serrano, J., Cordova, C., Kurz, S. C., Golfinos, J. G., Sulman, E. P., & others. (n.d.).

Publication year

2022

Journal title

Neuro-Oncology Advances

Volume

4

Issue

1

Page(s)

vdac163
Abstract
Abstract
~

Clinical, Pathological, and Molecular Characteristics of Diffuse Spinal Cord Gliomas

Feng, Y., Garcia, M. R., Feng, Y., Vasudevaraja, V., Galbraith, K., Serrano, J., Thomas, C., Radmanesh, A., Hidalgo, E. T., Harter, D. H., Allen, J. C., & others. (n.d.).

Publication year

2022

Journal title

Journal of Neuropathology & Experimental Neurology

Volume

81

Issue

11

Page(s)

865--872
Abstract
Abstract
~

Comments on: Statistical inference and large-scale multiple testing for high-dimensional regression models

Feng, Y., Tian, Y. e., & Feng, Y. (n.d.).

Publication year

2023

Journal title

Test

Volume

32

Issue

4

Page(s)

1172--1176
Abstract
Abstract
~

Community detection with nodal information : Likelihood and its variational approximation

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

Publication year

2022

Journal title

Stat

Volume

11

Issue

1
Abstract
Abstract
Community detection is one of the fundamental problems in the study of network data. Most existing community detection approaches only consider edge information as inputs, and the output could be suboptimal when nodal information is available. In such cases, it is desirable to leverage nodal information for the improvement of community detection accuracy. Towards this goal, we propose a flexible network model incorporating nodal information and develop likelihood-based inference methods. For the proposed methods, we establish favorable asymptotic properties as well as efficient algorithms for computation. Numerical experiments show the effectiveness of our methods in utilizing nodal information across a variety of simulated and real network data sets.

Comparison of solid tissue sequencing and liquid biopsy accuracy in identification of clinically relevant gene mutations and rearrangements in lung adenocarcinomas

Feng, Y., Lin, L. H., Allison, D. H., Feng, Y., Jour, G., Park, K., Zhou, F., Moreira, A. L., Shen, G., Feng, X., Sabari, J., & others. (n.d.).

Publication year

2021

Journal title

Modern Pathology

Volume

34

Issue

12

Page(s)

2168--2174
Abstract
Abstract
~

Consistent Estimation of the Number of Communities in Non-uniform Hypergraph Model

Shang, Z., Zhang, Z., & Feng, Y. (n.d.).

Publication year

2025

Journal title

Stat

Volume

14

Issue

2
Abstract
Abstract
We propose an algorithm based on cross-validation to estimate the number of communities in a general non-uniform hypergraph model. The algorithm involves a three-step process. Initially, it randomly divides the set of hyperedges into a training set and a testing set. Subsequently, for each candidate number of communities, we construct a spectral estimation of community labels and least square estimation of the hyperedge probabilities based on the training set. The final step involves the computation of cross-validation scores using the testing set. The proposed algorithm is shown to be consistent when the number of vertices tends to infinity.

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

Feng, Y., He, Y., Wu, R., Zhou, Y., & Feng, Y. (n.d.).

Publication year

2023

Journal title

Journal of the American Statistical Association

Page(s)

1--12
Abstract
Abstract
~

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

Feng, Y., Heng, S., Zhang, J., & Feng, Y. (n.d.).

Publication year

2023

Journal title

arXiv preprint arXiv:2310.18556
Abstract
Abstract
~

Differential Role of Hyperglycemia on Survival in IDH-wildtype Glioblastoma Subclasses

Feng, Y., Liu, E., Vasudevaraja, V., Sviderskiy, V., Feng, Y., Tran, I., Serrano, J., Cordova, C., Kurz, S., Golfinos, J., Sulman, E., & others. (n.d.). (6th eds.).

Publication year

2022

Volume

81

Page(s)

440--440
Abstract
Abstract
~

Discussion of “Cocitation and Coauthorship Networks of Statisticians”

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

Publication year

2022

Journal title

Journal of Business & Economic Statistics

Volume

40

Issue

2

Page(s)

486--490
Abstract
Abstract
~

Imbalanced classification: A paradigm-based review

Feng, Y., Feng, Y., Zhou, M., & Tong, X. (n.d.).

Publication year

2021

Journal title

Statistical Analysis and Data Mining: The ASA Data Science Journal

Volume

14

Issue

5

Page(s)

383--406
Abstract
Abstract
~

Large-scale model selection in misspecified generalized linear models

Feng, Y., Demirkaya, E., Feng, Y., Basu, P., & Lv, J. (n.d.).

Publication year

2022

Journal title

Biometrika

Volume

109

Issue

1

Page(s)

123--136
Abstract
Abstract
~

Learning from Similar Linear Representations: Adaptivity, Minimaxity, and Robustness

Feng, Y., Tian, Y. e., Gu, Y., & Feng, Y. (n.d.).

Publication year

2023

Journal title

arXiv preprint arXiv:2303.17765
Abstract
Abstract
~

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
~

Machine collaboration

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

Publication year

2024

Journal title

Stat

Volume

13

Issue

1

Page(s)

e661
Abstract
Abstract
~

Machine collaboration

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

Publication year

2024

Journal title

Stat

Volume

13

Issue

1
Abstract
Abstract
We propose a new ensemble framework for supervised learning, called machine collaboration (MaC), using a collection of possibly heterogeneous base learning methods (hereafter, base machines) for prediction tasks. Unlike bagging/stacking (a parallel and independent framework) and boosting (a sequential and top-down framework), MaC is a type of circular and recursive learning framework. The circular and recursive nature helps the base machines to transfer information circularly and update their structures and parameters accordingly. The theoretical result on the risk bound of the estimator from MaC reveals that the circular and recursive feature can help MaC reduce risk via a parsimonious ensemble. We conduct extensive experiments on MaC using both simulated data and 119 benchmark real datasets. The results demonstrate that in most cases, MaC performs significantly better than several other state-of-the-art methods, including classification and regression trees, neural networks, stacking, and boosting.

Contact

yang.feng@nyu.edu 708 Broadway New York, NY, 10003