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
Accounting for incomplete testing in the estimation of epidemic parameters
Failed retrieving data.Accounting for incomplete testing in the estimation of epidemic parameters
Failed retrieving data.Nested Model Averaging on Solution Path for High-dimensional Linear Regression
Failed retrieving data.Neyman-Pearson classification: parametrics and sample size requirement
Failed retrieving data.On the estimation of correlation in a binary sequence model
Failed retrieving data.On the sparsity of Mallows model averaging estimator
Failed retrieving data.A Kronecker Product Model for Repeated Pattern Detection on 2D Urban Images
Failed retrieving data.Likelihood adaptively modified penalties
Failed retrieving data.Regularization after retention in ultrahigh dimensional linear regression models
Failed retrieving data.The restricted consistency property of leave-$n_v$-out cross-validation for high-dimensional variable selection
Failed retrieving data.