Yang Feng
Yang Feng
Professor of Biostatistics
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Professional overview
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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.
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Education
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B.S. in Mathematics, University of Science and Technology of China, Hefei, ChinaPh.D. in Operations Research, Princeton University, Princeton, NJ
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Areas of research and study
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BioinformaticsBiostatisticsHigh-dimensional data analysis/integrationMachine learningModeling Social and Behavioral DynamicsNonparametric statistics
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Publications
Publications
Testing community structure for hypergraphs
AbstractFeng, Y., Yuan, M., Liu, R., Feng, Y., & Shang, Z. (n.d.).Publication year
2022Journal title
The Annals of StatisticsVolume
50Issue
1Page(s)
147--169Abstract~The Interplay of Demographic Variables and Social Distancing Scores in Deep Prediction of US COVID-19 Cases
AbstractFeng, Y., Tang, F., Feng, Y., Chiheb, H., & Fan, J. (n.d.).Publication year
2021Journal title
Journal of the American Statistical AssociationAbstract~The restricted consistency property of leave-$n_v$-out cross-validation for high-dimensional variable selection
AbstractFeng, Y., Feng, Y., & Yu, Y. i. (n.d.).Publication year
2019Journal title
Statistica SinicaVolume
29Page(s)
1607--1630Abstract~Towards the Theory of Unsupervised Federated Learning : Non-asymptotic Analysis of Federated EM Algorithms
AbstractTian, Y., Weng, H., & Feng, Y. (n.d.).Publication year
2024Journal title
Proceedings of Machine Learning ResearchVolume
235Page(s)
48226-48279AbstractWhile supervised federated learning approaches have enjoyed significant success, the domain of unsupervised federated learning remains relatively underexplored. Several federated EM algorithms have gained popularity in practice, however, their theoretical foundations are often lacking. In this paper, we first introduce a federated gradient EM algorithm (FedGrEM) designed for the unsupervised learning of mixture models, which supplements the existing federated EM algorithms by considering task heterogeneity and potential adversarial attacks. We present a comprehensive finite-sample theory that holds for general mixture models, then apply this general theory on specific statistical models to characterize the explicit estimation error of model parameters and mixture proportions. Our theory elucidates when and how FedGrEM outperforms local single-task learning with insights extending to existing federated EM algorithms. This bridges the gap between their practical success and theoretical understanding. Our numerical results validate our theory, and demonstrate FedGrEM’s superiority over existing unsupervised federated learning benchmarks.Transfer learning under high-dimensional generalized linear models
AbstractFeng, Y., Tian, Y. e., & Feng, Y. (n.d.).Publication year
2023Journal title
Journal of the American Statistical AssociationVolume
118Issue
544Page(s)
2684--2697Abstract~Unsupervised Federated Learning: A Federated Gradient EM Algorithm for Heterogeneous Mixture Models with Robustness against Adversarial Attacks
AbstractFeng, Y., Tian, Y. e., Weng, H., & Feng, Y. (n.d.).Publication year
2023Journal title
arXiv preprint arXiv:2310.15330Abstract~Unsupervised Multi-task and Transfer Learning on Gaussian Mixture Models
AbstractFeng, Y., Tian, Y. e., Weng, H., & Feng, Y. (n.d.).Publication year
2022Journal title
arXiv preprint arXiv:2209.15224Abstract~Variable selection for high-dimensional generalized linear model with block-missing data
AbstractFeng, Y., He, Y., Feng, Y., & Song, X. (n.d.).Publication year
2023Journal title
Scandinavian Journal of StatisticsVolume
50Issue
3Page(s)
1279--1297Abstract~Visceral adipose tissue in patients with COVID-19: risk stratification for severity
AbstractFeng, Y., Chandarana, H., Dane, B., Mikheev, A., Taffel, M. T., Feng, Y., & Rusinek, H. (n.d.).Publication year
2021Journal title
Abdominal RadiologyVolume
46Issue
2Page(s)
818--825Abstract~ℓ1-Penalized Multinomial Regression : Estimation, Inference, and Prediction, With an Application to Risk Factor Identification for Different Dementia Subtypes
AbstractTian, Y., Rusinek, H., Masurkar, A. V., & Feng, Y. (n.d.).Publication year
2024Journal title
Statistics in MedicineVolume
43Issue
30Page(s)
5711-5747AbstractHigh-dimensional multinomial regression models are very useful in practice but have received less research attention than logistic regression models, especially from the perspective of statistical inference. In this work, we analyze the estimation and prediction error of the contrast-based (Formula presented.) -penalized multinomial regression model and extend the debiasing method to the multinomial case, providing a valid confidence interval for each coefficient and (Formula presented.) value of the individual hypothesis test. We also examine cases of model misspecification and non-identically distributed data to demonstrate the robustness of our method when some assumptions are violated. We apply the debiasing method to identify important predictors in the progression into dementia of different subtypes. Results from extensive simulations show the superiority of the debiasing method compared to other inference methods.