Hao Huang, MD, MPH
Research Scientist in BiostatisticsCards
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Research Scientist in Biostatistics
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Dr. Huang is a Research Scientist at the department of Biostatistics, Yale School of Public Health. After completing his education in Medicine and Physiology at the First Military Medical School in China, he also completed his graduate education at Yale School of Public Health. His early studies focused on the basic research and study interests included the mechanism of neuronal death after ischemia, glia cell functions in the brain, neuronal circuit, mechanisms, and regulation of sleeping and food intake. More than 10 articles were published from these studies in prestigious journals, including Journal of Neuroscience, Journal of Physiology, etc. Since 2009, his study interests have been focused on clinical studies and public health. As one of the core members in the Data Coordinating Center (DCC) for Reproductive Medicine Network (RMN) and previous National Genomic and Proteomic Network at Yale, Dr. Huang have been actively involved in many important clinical trials, including the Genomic and Proteomic Network for Preterm Birth longitudinal, case-control, and expression profile studies, the Pregnancy in Polycystic Ovary Syndrome II (PPCOSII) study, the Assessment of multiple intrauterine gestations from ovarian stimulation (AMIGOS) study, and the Effects of Physiologic Oxygen Tension on Clinical In Vitro Fertilization Outcomes (PhOx) study. He played important roles in the study design, study initiation, data monitoring, meeting and conference report preparation, data management, data integration and harmonization, statistical analysis and manuscript publications. He has authored or co-authored more than 30 publications in prestigious journals, including 2 articles published in New England Journal of Medicine. Then, Dr. Huang and his colleagues have finished the trials of “Improving Reproductive Fitness Through Pretreatment with Lifestyle Modification in Obese Women with Unexplained Infertility (FIT-PLESE)”, the pilot trial “Males, Antioxidants, and Infertility Trial (MOXI)”, the Evaluation, Validation and Refinement of Noninvasive Diagnostic Biomarkers for Endometriosis (Endo Marker) study, the Optimal treatment for women with a Persisting Pregnancy of Unknown Location- Active Treatment versus Expectant Management (The “ACTorNOT TRIAL”); results from these studies have been published in prestigious journals, including the journal of JAMA and Plos Medicine. Currently, 2 clinical trials/studies (FRIEND and PREGnant) for the new reproductive medicine consortium are in active recruitment. Dr. Huang has been serving and now serves as the Data Director and Lead Statistician of the network/consortium. He also supervises all the secondary or ancillary analyses performed within the center and part of the network. In addition, he serves as the methodological editor for the prestigious journal of Fertility and Sterility.
Appointments
Biostatistics
Research ScientistPrimary
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Education & Training
- MPH
- Yale School of Public Health (2009)
- MS
- The First Military Medical University (2001)
- MD
- The First Military Medical University (1996)
Research
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Public Health Interests
ORCID
0000-0002-9103-4382
Research at a Glance
Yale Co-Authors
Publications Timeline
Heping Zhang, PhD
Hugh Taylor, MD
Martina Brueckner, MD
Valerie A. Flores
Angelique Bordey, PhD
David Glahn, PhD
Publications
2026
Developmental human brain connectome from fetal stage to early childhood
Wu W, Huang H, Zhu T, Tan S, Ouyang M. Developmental human brain connectome from fetal stage to early childhood. Developmental Cognitive Neuroscience 2026, 79: 101704. PMID: 41807894, DOI: 10.1016/j.dcn.2026.101704.Peer-Reviewed Original ResearchConceptsBrain connectomeDiffusion magnetic resonance imagingHuman brain connectomeFunctional brain networksBrain network topologySmall-world organizationHigher-order regionsRs-fMRIAtypical maturationShort-range connectionsConnectome studiesWhite matter fibersResting-state functional MRIEarly childhoodAberrant brain developmentPatterns of hyper-Hierarchical axisHub distributionBrain connectivityBrain networksConnectomeHypo-connectivityFunctional MRIFunctional connectivityDevelopmental perspectiveFunctional changes of precuneus architecture across newborns, infants, and early adolescents
Wang J, Peng Q, Ouyang M, Li R, Wu W, Zhang L, Peng Y, Huang H. Functional changes of precuneus architecture across newborns, infants, and early adolescents. Scientific Reports 2026 PMID: 41748807, DOI: 10.1038/s41598-026-40813-y.Peer-Reviewed Original ResearchConceptsAutism spectrum disorderDefault mode networkResting-state fMRIFunctional connectivityEarly adolescenceHigher-order association regionsStructural connectivityFunctional connectivity changesVisual-motor integrationSignificant developmental changesParcellation patternsConnectivity-based parcellationSocial cognitionMode networkBrain functional developmentCerebellum networkFunctional changesSpectrum disorderCortical gradientsSpatial cognitionConnectivity changesVisual-motorPCUNBrain disordersDevelopmental changesMachine learning to infer neurocognitive testing scores among adolescents and young adults with congenital heart disease
Hussain M, He S, Adams H, Anagnoustou E, Bellinger D, Brueckner M, Chung W, Cleveland J, Gelb B, Goldmuntz E, Hagler D, Huang H, McQuillen P, Miller T, Norris-Brilliant A, Porter G, Thomas N, Tivarus M, Xu D, Shen Y, Newburger J, Grant P, Morton S, Ou Y. Machine learning to infer neurocognitive testing scores among adolescents and young adults with congenital heart disease. Communications Medicine 2026, 6: 144. PMID: 41651962, DOI: 10.1038/s43856-026-01417-9.Peer-Reviewed Original ResearchConceptsHeart diseaseBackgroundCongenital heart diseaseCongenital heart diseaseMultivariate modelYoung adultsCognitive variablesInference scoresVerbal Comprehension IndexIndividual cognitive performanceNeurocognitive test scoresBrain imaging featuresCHDEnvironmental factorsNeurocognitive testsDigit spanMatrix ReasoningWorking memoryGeneral intelligencePerceptual reasoningScoresBrainBirthCognitive performanceDiseaseMeaningful inferencesHierarchical maturation of structural brain connectomes from birth to childhood
Zhao T, Ouyang M, Shou X, Zhang S, Ju J, Liao X, Han M, Sun L, Wang X, Xia Y, Hu D, Kang H, Guo J, Wang Q, Li M, Huo R, Liu Y, Yuan H, Peng Y, Huang H, He Y. Hierarchical maturation of structural brain connectomes from birth to childhood. Nature Communications 2026, 17: 1945. PMID: 41571660, PMCID: PMC12929715, DOI: 10.1038/s41467-026-68704-w.Peer-Reviewed Original ResearchCitationsMeSH Keywords and ConceptsStructural and Functional Coupling in Neonates and its Prediction for Neurocognitive Outcomes
Xu Y, Zhao T, Jeon T, Ouyang M, Zhu T, Chalak L, Rollins N, Huang H. Structural and Functional Coupling in Neonates and its Prediction for Neurocognitive Outcomes. Journal Of Pacific Rim Psychology 2026, 20 DOI: 10.1177/18344909251414889.Peer-Reviewed Original ResearchConceptsStructure-function couplingCognitive developmentNeurocognitive outcomesSupport vector regression analysisVector regression analysisLanguage scoresLimbic systemParietal regionsTemporal lobeFunctional couplingHuman brainFunctional networksYears of ageOccipital lobeMRI dataFunctional interactionsMulti-modal MRI dataStructural networkPrenatal periodLobeNeurocognitionDiffusion‐MRI‐Based Estimation of Cortical Architecture via Machine Learning (DECAM) in Primate Brains
Zhu T, Ouyang M, Tan S, Guo J, Zhang Z, Liu X, Liu R, Huang H. Diffusion‐MRI‐Based Estimation of Cortical Architecture via Machine Learning (DECAM) in Primate Brains. Advanced Science 2026, 13: e12752. PMID: 41486548, PMCID: PMC12970224, DOI: 10.1002/advs.202512752.Peer-Reviewed Original ResearchMeSH Keywords and ConceptsConceptsDiffusion MRIDiffusion MRI signal modellingPrimate brainCortical architectureDeep learning frameworkMulti-shell diffusion MRILabel vectorLearning frameworkDensity mapsNon-human primate brainHigh-resolutionMachine learningHistology datasetMachine-learningTranslation frameworkHigh-fidelityPrimates
2025
Mapping Functional Brain Organization Using Artificial Intelligence
Zhu T, Mohapatra S, Tan S, Ouyang M, Huang H. Mapping Functional Brain Organization Using Artificial Intelligence. Chemical & Biomedical Imaging 2025 DOI: 10.1021/cbmi.5c00092.Peer-Reviewed Original ResearchConceptsFunctional brain organizationArtificial intelligenceBrain organizationNeural networkSelf-supervised learning frameworkGraph neural networksTemporal feature extractionConvolutional neural networkFunctional parcellationResting-state functional MRITask-based fMRIAI-based methodsAI-based approachesBrain network architectureNetwork architectureFeature extractionTransformer networkLearning frameworkBrain regionsFunctional MRIFMRI dataRs-fMRIResting-stateCross-modality validityConnectivity patternsTReND: Transformer Derived Features and Regularized NMF for Neonatal Functional Network Delineation
Mohapatra S, Ouyang M, Tan S, Guo J, Sun L, He Y, Huang H. TReND: Transformer Derived Features and Regularized NMF for Neonatal Functional Network Delineation. Lecture Notes In Computer Science 2025, 15971: 660-669. DOI: 10.1007/978-3-032-05162-2_63.Peer-Reviewed Original ResearchCitationsConceptsRegularized Nonnegative Matrix FactorizationTemporal featuresFunctional networksSelf-attention layerSpatial coherenceResting-state fMRIRs-fMRIRs-fMRI dataNonnegative matrix factorizationSpatial encodingRs-fMRI datasetLatent embeddingsIn vivo explorationClustering techniqueGeodesic distanceSmoothness constraintSpatiotemporal featuresMatrix factorizationClustering approachAccurate prediction of 2-year-old neurodevelopmental outcomes in mild HIE with neonatal white matter microstructure.
Mohapatra S, Wu W, Sindabizera K, Chalak L, Huang H, Ouyang M. Accurate prediction of 2-year-old neurodevelopmental outcomes in mild HIE with neonatal white matter microstructure. Proceedings Of The International Society For Magnetic Resonance In Medicine ... Scientific Meeting And Exhibition. 2025 DOI: 10.58530/2025/1041.Peer-Reviewed Original ResearchConceptsMild hypoxic-ischemic encephalopathyHypoxic-ischemic encephalopathyNeurodevelopmental outcomesModerate hypoxic-ischemic encephalopathyWhite matterAged 2White matter tract alterationsWM microstructureLong-term disabilityNeonatal mortalityAdverse outcomesTract alterationsPatient outcomesWhite matter microstructureEarly identificationWM tractsOutcomesDiffusion metricsBirthAlterationsAgeMicrostructural alterationsPatientsEncephalopathyEvaluating the Effect of Epigallocatechin Gallate (EGCG) in Reducing Folate Levels in Reproductive Aged Women by MTHFR and DHFR Genotype in Combination With Letrozole or Clomiphene
Johnson J, Siblini H, Al‐Hendy A, Segars J, González F, Taylor H, Singh B, Carson S, Christman G, Huang H, Dangi B, Zhang H. Evaluating the Effect of Epigallocatechin Gallate (EGCG) in Reducing Folate Levels in Reproductive Aged Women by MTHFR and DHFR Genotype in Combination With Letrozole or Clomiphene. Clinical And Translational Science 2025, 18: e70189. PMID: 40077973, PMCID: PMC11903501, DOI: 10.1111/cts.70189.Peer-Reviewed Original ResearchCitationsMeSH Keywords and ConceptsConceptsEpigallocatechin-3-gallateFolate levelsUterine fibroidsClinical trialsGreen tea extractAssociated with folate deficiencyPresence of MTHFRWomen of Childbearing AgeSerum folate levelsReduced folate levelsActive clinical trialsReproductive-age womenEffect of epigallocatechin gallateDHFR genotypeDHFR polymorphismTea extractDaily doseClomiphene citrateUnexplained infertilityFolate deficiencyChildbearing ageClinical studiesTreated womenFibroidsLetrozole
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