Yuan Huang, PhD
Assistant Professor of Biostatistics (Biostatistics)DownloadHi-Res Photo
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Assistant Professor of Biostatistics (Biostatistics)
Biography
Yuan Huang is an Assistant Professor in the Department of Biostatistics at Yale School of Public Health. Her methodological research is focused on statistical methods for high-dimensional data and has been motivated by challenges posted by analyzing cancer genomics, such as low reproducibly, nonlinearity, and heterogeneity. Applications from her work include biomarker identification, large-scale network structure estimation, GxE analysis, etc. She is particularly interested in integrative analysis that simultaneously analyzes multiple datasets to improve the discovery. Recently she collaborates extensively in neurodegenerative diseases, such as Huntington’s disease and multiple sclerosis. She is also actively involved in collaborative research on clinical trials, genetics, epidemiology, and other biomedical fields.
Appointments
Biostatistics
Assistant ProfessorPrimary
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Education & Training
- PhD
- The Pennsylvania State University, Statistics
Research
Overview
Public Health Interests
Aging; Biomarkers; Cancer; Chronic Diseases; Genetics, Genomics, Epigenetics
ORCID
0000-0002-5218-9269
Research at a Glance
Publications Timeline
A big-picture view of Yuan Huang's research output by year.
7Publications
44Citations
Publications
2024
Deep Dimension Reduction for Supervised Representation Learning
Huang J, Jiao Y, Liao X, Liu J, Yu Z. Deep Dimension Reduction for Supervised Representation Learning. IEEE Transactions On Information Theory 2024, 70: 3583-3598. DOI: 10.1109/tit.2023.3340658.Peer-Reviewed Original ResearchConceptsRepresentation learningStandard deep learning modelsHigh-dimensional complex dataSupervised representation learningRepresentation learning tasksDeep neural networksEffective data representationsContext of classificationDeep learning modelsNonparametric representationDimension reduction methodDimension reduction approachLearned representationsPromote disentanglementData representationNeural networkComplex dataLearning modelsDimension reductionTarget representationLearning tasksReduction methodSufficient dimension reduction methodsLow-dimensionalConditional independence
2023
Online inference in high-dimensional generalized linear models with streaming data.
Luo L, Han R, Lin Y, Huang J. Online inference in high-dimensional generalized linear models with streaming data. Electronic Journal Of Statistics 2023, 17: 3443-3471. PMID: 39188774, PMCID: PMC11346802, DOI: 10.1214/23-ejs2182.Peer-Reviewed Original ResearchCitationsOnline inference with debiased stochastic gradient descent
Han R, Luo L, Lin Y, Huang J. Online inference with debiased stochastic gradient descent. Biometrika 2023, 111: 93-108. DOI: 10.1093/biomet/asad046.Peer-Reviewed Original ResearchCitationsConceptsStochastic gradient descent algorithmHigh-dimensional statisticsOne-pass algorithmGradient descent algorithmHigh-dimensional dataAsymptotic normalityText datasetsSparsity levelOnline fashionOnline inferenceData distributionTime complexitySpace complexityDescent algorithmStatistical inferenceUpdate stepNumerical experimentsAlgorithmDebiasing techniquesMild conditionsInferenceSparsityEstimationConfidence intervalsDatasetHETEROGENEITY ANALYSIS VIA INTEGRATING MULTI-SOURCES HIGH-DIMENSIONAL DATA WITH APPLICATIONS TO CANCER STUDIES.
Zhong T, Zhang Q, Huang J, Wu M, Ma S. HETEROGENEITY ANALYSIS VIA INTEGRATING MULTI-SOURCES HIGH-DIMENSIONAL DATA WITH APPLICATIONS TO CANCER STUDIES. Statistica Sinica 2023, 33: 729-758. PMID: 38037567, PMCID: PMC10686523, DOI: 10.5705/ss.202021.0002.Peer-Reviewed Original ResearchCitationsAltmetric
2021
Regularized projection score estimation of treatment effects in high-dimensional quantile regression
Cheng C, Feng X, Huang J, Liu X. Regularized projection score estimation of treatment effects in high-dimensional quantile regression. Statistica Sinica 2021 DOI: 10.5705/ss.202019.0247.Peer-Reviewed Original ResearchCitations
2017
A group adaptive elastic-net approach for variable selection in high-dimensional linear regression
Hu J, Huang J, Qiu F. A group adaptive elastic-net approach for variable selection in high-dimensional linear regression. Science China Mathematics 2017, 61: 173-188. DOI: 10.1007/s11425-016-0071-x.Peer-Reviewed Original ResearchCitationsConceptsAdaptive elastic-netHigh-dimensional linear regressionProblem of group selectionElastic-netOracle propertyOracle inequalitiesHigh-dimensional problemsVariable selectionGroup structureSample sizeModel selectionCollinearity problemElastic netOracleElastic-net approachHigh-dimensionalCompetitive methodsData studiesLinear regression modelsProblemInequalityModel consistencyGroup numberStatistical modelInference
2015
Asymptotic properties of Lasso in high-dimensional partially linear models
Ma C, Huang J. Asymptotic properties of Lasso in high-dimensional partially linear models. Science China Mathematics 2015, 59: 769-788. DOI: 10.1007/s11425-015-5093-2.Peer-Reviewed Original ResearchCitationsConceptsHigh-dimensional partially linear modelsPartially linear modelsLinear partPerformance of variable selectionFinite sample performanceNonparametric function estimationRate of convergenceTruncated series expansionNonparametric componentAsymptotic propertiesNonparametric functionOracle inequalitiesRegularity conditionsSufficient conditionsLasso estimatorPolynomial splinesFunction estimationSparsity assumptionLinear modelVariable selectionSimulation studySeries expansionEstimation errorRegression coefficientsLinear component
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