Yang Feng | NYU School of Global Public Health

Yang Feng

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Professor of Biostatistics

Yang Feng is an Associate Professor of Biostatistics. He received his B.S. in mathematics from the University of Science and Technology of China and his Ph.D. in Operations Research from Princeton University.

Dr. Feng's research interests include machine learning with applications to public health, high-dimensional statistics, network models, nonparametric statistics, and bioinformatics. He has published in The Annals of Statistics, Journal of the American Statistical Association, Journal of the Royal Statistical Society Series B, Journal of Machine Learning Research, International Journal of Epidemiology, and Science Advances. Feng serves on the editorial boards of the Journal of Business & Economic Statistics, Statistica Sinica, Stat, and Statistical Analysis and Data Mining: The ASA Data Science Journal.

Prior to joining NYU, Feng was an Associate Professor of Statistics and an affiliated member in the Data Science Institute at Columbia University. He is an elected member of the International Statistical Institute and a recipient of the NSF CAREER award.

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

A flexible quasi-likelihood model for microbiome abundance count data

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

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

PCABM: Pairwise Covariates-Adjusted Block Model for Community Detection

RaSE: A Variable Screening Framework via Random Subspace Ensembles

Simulation of New York City's Ventilator Allocation Guideline during the Spring 2020 COVID-19 Surge

Walsh, B. C., Zhu, J., Feng, Y., Berkowitz, K. A., Betensky, R. A., Nunnally, M. E., & Pradhan, D. R. (n.d.).

Publication year

2023

Journal title

JAMA network open

Volume

Issue

Page(s)

E2336736

10.1001/jamanetworkopen.2023.36736

Abstract

Abstract

Importance: The spring 2020 surge of COVID-19 unprecedentedly strained ventilator supply in New York City, with many hospitals nearly exhausting available ventilators and subsequently seriously considering enacting crisis standards of care and implementing New York State Ventilator Allocation Guidelines (NYVAG). However, there is little evidence as to how NYVAG would perform if implemented. Objectives: To evaluate the performance and potential improvement of NYVAG during a surge of patients with respect to the length of rationing, overall mortality, and worsening health disparities. Design, Setting, and Participants: This cohort study included intubated patients in a single health system in New York City from March through July 2020. A total of 20000 simulations were conducted of ventilator triage (10000 following NYVAG and 10000 following a proposed improved NYVAG) during a crisis period, defined as the point at which the prepandemic ventilator supply was 95% utilized. Exposures: The NYVAG protocol for triage ventilators. Main Outcomes and Measures: Comparison of observed survival rates with simulations of scenarios requiring NYVAG ventilator rationing. Results: The total cohort included 1671 patients; of these, 674 intubated patients (mean [SD] age, 63.7 [13.8] years; 465 male [69.9%]) were included in the crisis period, with 571 (84.7%) testing positive for COVID-19. Simulated ventilator rationing occurred for 163.9 patients over 15.0 days, 44.4% (95% CI, 38.3%-50.0%) of whom would have survived if provided a ventilator while only 34.8% (95% CI, 28.5%-40.0%) of those newly intubated patients receiving a reallocated ventilator survived. While triage categorization at the time of intubation exhibited partial prognostic differentiation, 94.8% of all ventilator rationing occurred after a time trial. Within this subset, 43.1% were intubated for 7 or more days with a favorable SOFA score that had not improved. An estimated 60.6% of these patients would have survived if sustained on a ventilator. Revising triage subcategorization, proposed improved NYVAG, would have improved this alarming ventilator allocation inefficiency (25.3% [95% CI, 22.1%-28.4%] of those selected for ventilator rationing would have survived if provided a ventilator). NYVAG ventilator rationing did not exacerbate existing health disparities. Conclusions and Relevance: In this cohort study of intubated patients experiencing simulated ventilator rationing during the apex of the New York City COVID-19 2020 surge, NYVAG diverted ventilators from patients with a higher chance of survival to those with a lower chance of survival. Future efforts should be focused on triage subcategorization, which improved this triage inefficiency, and ventilator rationing after a time trial, when most ventilator rationing occurred..

Spectral Clustering via Adaptive Layer Aggregation for Multi-Layer Networks

Transfer Learning Under High-Dimensional Generalized Linear Models

Variable selection for high-dimensional generalized linear model with block-missing data

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

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

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

Community detection with nodal information: Likelihood and its variational approximation

Large-scale model selection in misspecified generalized linear models

Model Averaging for Nonlinear Regression Models

Targeting Predictors Via Partial Distance Correlation With Applications to Financial Forecasting

TESTING COMMUNITY STRUCTURE FOR HYPERGRAPHS

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

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

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