708 Broadway, Room 415
Department of Biostatistics
New York University
New York, NY 10003
Email: yf31@nyu.edu
Professor · Biostatistics
Yang Feng
Statistical machine learning and high-dimensional inference for biomedical discovery.
New York University · School of Global Public Health · Affiliate, Center for Data Science
Overview
I am a Professor of Biostatistics in the School of Global Public Health at New York University and an affiliated faculty member at the Center for Data Science. My research focuses on the theoretical and methodological foundations of machine learning, high-dimensional statistics, network models, and nonparametric methods, with applications in Alzheimer’s disease prognosis, cancer subtype classification, genomics, electronic health records, and biomedical imaging.
I lead the Feng Lab, which is dedicated to advancing statistical learning, data science, and AI through rigorous research and impactful applications. For a short, conference-style biography, see the Bio page.
📢 Message to Prospective PhD Students
Thank you very much for your interest in pursuing a PhD and for considering working with me. Please note that PhD admissions at NYU Biostatistics are handled at the departmental level rather than by individual faculty. You are welcome to mention my name in your application if you believe our research interests align. Due to the large volume of inquiries I receive each year, I sincerely apologize that I am not able to respond to individual inquiry emails. I appreciate your understanding and wish you the very best in your application process.
Research at a glance
Machine Learning
Transfer, multi-task and federated learning; Neyman–Pearson classification; causal inference; deep learning.
High-Dimensional Statistics
Variable selection and screening, Gaussian graphical models, inference under high dimensions.
Network Models
Community detection, network embedding, statistical inference on graphs.
Applications
Electronic health records, genomics, epidemiology, neuroscience, social networks, computer vision.
Signature work
Semiparametric Modeling and Analysis for Longitudinal Network Data
A semiparametric framework for networks observed over time that lets connection patterns evolve smoothly rather than forcing every edge to follow a fixed parametric law.
AoS · 2025Neyman-Pearson Multi-Class Classification via Cost-Sensitive Learning
Extends the Neyman-Pearson paradigm — controlling the specific error you care about most — from binary to many classes, by recasting it as a cost-sensitive learning problem with guarantees on the user-specified error budget.
JASA · 2024Transfer Learning under High-Dimensional Generalized Linear Models
Borrow strength from related high-dimensional generalized linear models to estimate a target model with provably minimax-optimal rates, together with a data-driven test that filters out source datasets that would actually hurt.
JASA · 2023PCABM: Pairwise Covariates-Adjusted Block Model for Community Detection
A community-detection model that accounts for node-pair covariates partly explaining who connects to whom, blending block structure with regression-style adjustment for the part of connectivity the covariates already explain.
JASA · 2023Testing Community Structure for Hypergraphs
A likelihood-ratio test for whether a hypergraph contains more than one community, with sharp boundaries on when communities are statistically detectable that improve over methods designed only for ordinary graphs.
AoS · 2022RaSE: Random Subspace Ensemble Classification
A flexible high-dimensional classification framework that aggregates many randomly sampled low-dimensional subspace classifiers, with consistency theory and a CRAN package.
JMLR · 2021Neyman-Pearson Classification Algorithms and NP Receiver Operating Characteristics
A practical algorithm that controls the Type-I error of any classifier with prescribed high probability, plus an NP-ROC curve for comparing classifiers under asymmetric error costs.
Sci. Adv. · 2018Model Selection for High-Dimensional Quadratic Regression via Regularization
A regularized model-selection method for high-dimensional regression with interaction terms that respects the strong-heredity principle: an interaction is only chosen when both its main effects are also present. A weak-heredity version is also proposed that only requires one of the main effects to be present.
JASA · 2018Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Additive Models
Extends sure-independence screening to additive nonparametric models, drastically reducing ultra-high-dimensional feature pools while retaining all the truly relevant variables with high probability.
JASA · 2011Network Exploration via the Adaptive LASSO and SCAD Penalties
Estimate the structure of a sparse Gaussian graphical model — the network of conditional dependencies among many variables — using adaptive LASSO and SCAD penalties, with oracle-property guarantees on variable selection.
AoAS · 2009Feng Lab
Join the Feng Lab
The lab brings together PhD students, postdocs, and collaborators working on the statistical foundations of machine learning and their biomedical applications. Recent alumni have placed at top universities and industry research labs.
Recent updates
News
| Jun 21, 2026 | Paper accepted at JASA |
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| Apr 18, 2026 | Paper accepted at JASA |
| Sep 14, 2025 | Review Editor Appointment for JASA and TAS |
Latest Posts
| Nov 10, 2025 | Using NYU HPC Greene: A Beginner-Friendly Tutorial for R Users |
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| Dec 04, 2024 | My Spanish Learning Journey |
Academic Appointments
- Professor, Department of Biostatistics, School of Global Public Health, New York University
- Visiting Professor, Department of Statistics and Data Science, The Wharton School, University of Pennsylvania (Fall 2025)
- Affiliate Faculty, Center for Data Science, New York University
- Affiliate Faculty, PRIISM, New York University
Education
- PhD in Operations Research, Princeton University (2006–2010)
- BS in Mathematics, University of Science and Technology of China (2002–2006)
Editorial Activities
- Review Editor (2026–2028), Journal of the American Statistical Association (JASA) and The American Statistician (TAS)
- Associate Editor, Journal of the American Statistical Association (JASA), Theory and Methods
- Associate Editor, Annals of Applied Statistics (AOAS)
- Associate Editor, Journal of Business & Economic Statistics (JBES)
- Associate Editor, Journal of Computational & Graphical Statistics (JCGS)
- Associate Editor, Statistical Learning and Data Science (SLADS)
Selected Honors and Awards
- Faculty of the Year Award, NYU School of Global Public Health, 2025
- Teaching Excellence Award, NYU School of Global Public Health, 2024
- Fellow, Institute of Mathematical Statistics (IMS), 2023
- Fellow, American Statistical Association (ASA), 2022
- Elected Member, International Statistical Institute (ISI), 2017
- NSF CAREER Award, National Science Foundation (NSF), 2016
Research Support & Grants
- NSF Grant DMS-2324489: Collaborative Research: New Theory and Methods for High-Dimensional Multi-Task and Transfer Learning Inference