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

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

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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

10.1002/sta4.428

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.

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

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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

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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

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Model Averaging for Nonlinear Regression Models

Feng, Y., Liu, Q., Yao, Q., & Zhao, G. (n.d.).

Publication year

2022

Journal title

Journal of Business and Economic Statistics

Volume

40

Issue

2

Page(s)

785-798

10.1080/07350015.2020.1870477

Abstract

Abstract

This article considers the problem of model averaging for regression models that can be nonlinear in their parameters and variables. We consider a nonlinear model averaging (NMA) framework and propose a weight-choosing criterion, the nonlinear information criterion (NIC). We show that up to a constant, NIC is an asymptotically unbiased estimator of the risk function under nonlinear settings with some mild assumptions. We also prove the optimality of NIC and show the convergence of the model averaging weights. Monte Carlo experiments reveal that NMA leads to relatively lower risks compared with alternative model selection and model averaging methods in most situations. Finally, we apply the NMA method to predicting the individual wage, where our approach leads to the lowest prediction errors in most cases.

Spectral clustering via adaptive layer aggregation for multi-layer networks

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

Publication year

2022

Journal title

Journal of Computational and Graphical Statistics

Issue

just-accepted

Page(s)

1--35

Abstract

Abstract

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Targeting predictors via partial distance correlation with applications to financial forecasting

Feng, Y., Yousuf, K., & Feng, Y. (n.d.).

Publication year

2022

Journal title

Journal of Business & Economic Statistics

Volume

40

Issue

3

Page(s)

1007--1019

Abstract

Abstract

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Testing community structure for hypergraphs

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

Publication year

2022

Journal title

The Annals of Statistics

Volume

50

Issue

1

Page(s)

147--169

Abstract

Abstract

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Unsupervised Multi-task and Transfer Learning on Gaussian Mixture Models

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

Publication year

2022

Journal title

arXiv preprint arXiv:2209.15224

Abstract

Abstract

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A demonstration of the RaSEn package

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

Publication year

2021

Abstract

Abstract

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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

10.1016/j.jim.2020.112909

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

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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

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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

~

Mediation effect selection in high-dimensional and compositional microbiome data

Feng, Y., Zhang, H., Chen, J., Feng, Y., Wang, C., Li, H., & Liu, L. (n.d.).

Publication year

2021

Journal title

Statistics in medicine

Volume

40

Issue

4

Page(s)

885--896

Abstract

Abstract

~

NCOG-11. ASSOCIATION OF HYPERGLYCEMIA AND TUMOR SUBCLASS ON SURVIVAL IN IDH-WILDTYPE GLIOBLASTOMA

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.).

Publication year

2021

Journal title

Neuro-Oncology

Volume

23

Issue

Suppl 6

Page(s)

vi154

Abstract

Abstract

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Neyman-Pearson Multi-class Classification via Cost-sensitive Learning

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

Publication year

2021

Journal title

arXiv preprint arXiv:2111.04597

Abstract

Abstract

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RaSE: A Variable Screening Framework via Random Subspace Ensembles

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

Publication year

2021

Journal title

Journal of American Statistical Association

Abstract

Abstract

~

RaSE: Random Subspace Ensemble Classification

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

Publication year

2021

Journal title

Journal of Machine Learning Research

Abstract

Abstract

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Super RaSE: Super Random Subspace Ensemble Classification

Feng, Y., Zhu, J., & Feng, Y. (n.d.).

Publication year

2021

Journal title

Journal of Risk and Financial Management

Volume

14

Issue

12

Page(s)

612

Abstract

Abstract

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Targeted crisis risk control: A neyman-pearson approach

Feng, Y., Feng, Y., Tong, X., & Xin, W. (n.d.).

Publication year

2021

Journal title

Available at SSRN

Abstract

Abstract

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

Feng, Y., Tang, F., Feng, Y., Chiheb, H., & Fan, J. (n.d.).

Publication year

2021

Journal title

Journal of the American Statistical Association

Abstract

Abstract

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Visceral adipose tissue in patients with COVID-19: risk stratification for severity

Feng, Y., Chandarana, H., Dane, B., Mikheev, A., Taffel, M. T., Feng, Y., & Rusinek, H. (n.d.).

Publication year

2021

Journal title

Abdominal Radiology

Volume

46

Issue

2

Page(s)

818--825

Abstract

Abstract

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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

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