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

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

Multi-label Random Subspace Ensemble Classification

Neyman-Pearson Multi-Class Classification via Cost-Sensitive Learning

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

Machine collaboration

Omics feature selection with the extended SIS R package: identification of a body mass index epigenetic multimarker in the Strong Heart Study

PCABM: Pairwise Covariates-Adjusted Block Model for Community Detection

Prognostic value of DNA methylation subclassification, aneuploidy, and CDKN2A/B homozygous deletion in predicting clinical outcome of IDH mutant astrocytomas

Racial distribution of molecularly classified brain tumors

ℓ1-Penalized Multinomial Regression: Estimation, Inference, and Prediction, With an Application to Risk Factor Identification for Different Dementia Subtypes

A flexible quasi-likelihood model for microbiome abundance count data

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

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

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