Shuangge Steven Ma, PhD
Department Chair and Professor of BiostatisticsDownloadHi-Res Photo
Cards
Education
PhD
University of Wisconsin (2004)
University of Wisconsin (2004)
MS
University of California at Los Angeles (2000)
University of California at Los Angeles (2000)
Contact Info
Training
Postdoctoral Associate
University of Washington (2006)
University of Washington (2006)
Education
PhD
University of Wisconsin (2004)
University of Wisconsin (2004)
MS
University of California at Los Angeles (2000)
University of California at Los Angeles (2000)
Contact Info
Training
Postdoctoral Associate
University of Washington (2006)
University of Washington (2006)
Education
PhD
University of Wisconsin (2004)
University of Wisconsin (2004)
MS
University of California at Los Angeles (2000)
University of California at Los Angeles (2000)
Contact Info
Training
Postdoctoral Associate
University of Washington (2006)
University of Washington (2006)
About
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Titles
Department Chair and Professor of Biostatistics
Biography
Dr. Ma received his Ph.D. degree in statistics at University of Wisconsin in 2004. Prior to arriving at Yale, Dr. Ma was a Senior Fellow in Collaborative Health Studies Coordinating Center (CHSCC) and Department of Biostatistics at University of Washington. He has been involved in developing novel statistical and bioinformatics methodologies for analysis of cancer (NHL, breast cancer, melanoma, lung cancer), mental disorders, and cardiovascular diseases. He has also been involved in health economics research, with special interest in health insurance in developing countries.
Last Updated on April 07, 2025.
Appointments
Biostatistics
ChairDualBiostatistics
ProfessorPrimary
Other Departments & Organizations
- Biostatistics
- Cancer Prevention and Control
- Center for Infection and Immunity
- Computational Biology and Biomedical Informatics
- Ma Lab
- SPORE in Lung Cancer
- SPORE in Skin Cancer
- Yale Cancer Center
- Yale Combined Program in the Biological and Biomedical Sciences (BBS)
- Yale Institute for Global Health
- Yale School of Public Health
- Yale Ventures
- Yale-BI Biomedical Data Science Fellowship
- YSPH Global Health Concentration
Education & Training
- Postdoctoral Associate
- University of Washington (2006)
- PhD
- University of Wisconsin (2004)
- MS
- University of California at Los Angeles (2000)
Research
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Overview
Develop novel statistical methodologies for complex data;
Study epidemiology and pathogenesis of multiple cancers, including breast cancer, NHL, melanoma and lung cancer;
Conduct survey studies, investigating health insurance utilization and impact;
Provide statistical support to multiple biomedical studies.
Medical Research Interests
Biostatistics; Economics; Neoplasms
Public Health Interests
Health Policy; Mental Health; Bioinformatics; Biomarkers; Cancer; Genetics, Genomics, Epigenetics; Cardiovascular Diseases
Research at a Glance
Research Interests
Research topics Shuangge Steven Ma is interested in exploring.
Publications
2026
Tree-based heterogeneous block Gaussian graphical models
Zeng X, Ma S, Zhang Q. Tree-based heterogeneous block Gaussian graphical models. Computational Statistics & Data Analysis 2026, 222: 108399. DOI: 10.1016/j.csda.2026.108399.Peer-Reviewed Original ResearchA high-dimensional additive model with a nonparametric extension of Box–Cox transformations
Liang W, Zhang Q, Ma S. A high-dimensional additive model with a nonparametric extension of Box–Cox transformations. Statistics & Probability Letters 2026, 236: 110756. DOI: 10.1016/j.spl.2026.110756.Peer-Reviewed Original ResearchSupervised Heterogeneous Gaussian Graphical Models
Zeng X, Ma S, Zhang Q. Supervised Heterogeneous Gaussian Graphical Models. Journal Of Computational And Graphical Statistics 2026, ahead-of-print: 1-10. DOI: 10.1080/10618600.2026.2653759.Peer-Reviewed Original ResearchConceptsExpectation-conditional-maximizationReal-world scenariosGaussian graphical modelsUnsupervised learningAnalysis of spectrometric dataHeterogeneous dataGraphical modelsSuperior performanceDiscrete response variablesMixture regression modelHeterogeneity analysis methodPredictive performanceAlgorithmSubgroup structureExpectation-conditional-maximization algorithmSupplementary materialsGraphs/networksPerformanceNumerical studyResponse variablesLearningOnlineAnalysis methodScenariosModelVariable selection and inference with a new robust Bayesian elastic net
Lu X, Ren J, Ma S, Wu C. Variable selection and inference with a new robust Bayesian elastic net. Journal Of Statistical Computation And Simulation 2026, ahead-of-print: 1-25. DOI: 10.1080/00949655.2026.2666569.Peer-Reviewed Original ResearchConceptsHeavy-tailed errorsBayesian elastic netStatistical inferenceVariable selectionSpike-and-slab priorsHigh-dimensional genomic studiesElastic netBayesian credible intervalsMetropolis-within-GibbsStatistical inference proceduresFinite samplesShrinkage estimatorsInference proceduresLikelihood functionRobust likelihood functionCredible intervalsBayesian hierarchical modelPosterior estimatesBayesian methodsNurses' Health StudyNumerical studyInferenceRobust elastic netStructured sparsityHierarchical modelMedicare Insurance Type and Broad Genomic Profiling in Metastatic Cancer
Chow R, Rothen J, Long J, Soulos P, Wang X, Mamtani R, Ma S, Kunst N, Dinan M, Gross C. Medicare Insurance Type and Broad Genomic Profiling in Metastatic Cancer. JAMA Network Open 2026, 9: e2614919. PMID: 42201732, PMCID: PMC13216988, DOI: 10.1001/jamanetworkopen.2026.14919.Peer-Reviewed Original ResearchAltmetricMeSH Keywords and ConceptsConceptsHospital referral regionsMedian odds ratioMedicare beneficiariesCohort studyMedicare AdvantagePayer typeMA beneficiariesOdds ratioStudy of Medicare beneficiariesCohort study of Medicare beneficiariesMedicare insurance typeMetastatic cancerLogistic regression modelsRetrospective cohort studyFFS beneficiariesReferral regionsMain OutcomesDiagnosis of metastatic cancerGenomic profilingGuideline recommendationsInsurance typeMedicarePrimary outcomeService recordsRegression modelsCausal links among gut microbiota, immune-inflammatory and compensatory immune-regulatory systems, and schizophrenia
Yang B, He L, Liu T, Ma S, Wu Y, Wen L, Emu B. Causal links among gut microbiota, immune-inflammatory and compensatory immune-regulatory systems, and schizophrenia. Neuropsychopharmacology 2026, 1-12. PMID: 41986467, PMCID: PMC13215341, DOI: 10.1038/s41386-026-02407-0.Peer-Reviewed Original ResearchCitationsAltmetricConceptsGenome-wide association studiesGut microbiotaImmune traitsEuropean ancestry cohortsAncestry cohortsGut microbesAssociation studiesBacterial generaTumor necrosis factor receptor 1Compensatory immune-regulatory systemGordonibacterClostridium innocuumMendelian randomizationGenera BarnesiellaImmune-inflammatory response systemBiological relevanceGutRisk effectsBarnesiellaMicrobiotaTraitsSchizophrenia pathogenesisTherapeutic targetDesulfovibrioRisk of schizophreniaHeterogeneous Gene Network Estimation for Single-Cell Transcriptomic Data via a Joint Regularized Deep Neural Network
Yang J, Li T, Wang T, Ma S, Wu M. Heterogeneous Gene Network Estimation for Single-Cell Transcriptomic Data via a Joint Regularized Deep Neural Network. Journal Of The American Statistical Association 2026, ahead-of-print: 1-12. DOI: 10.1080/01621459.2026.2615185.Peer-Reviewed Original ResearchCitationsConceptsSingle-cell transcriptomic dataStatistical enrichmentTranscriptome dataEnrichment of biological processesCellular heterogeneityGene network estimationState-of-the-art methodsRegularized deep neural networkDeep neural network methodState-of-the-artDeep neural networksSingle-cell resolutionNetwork estimationGene networksNeural network methodHub genesK-means clusteringNetwork constructionBiological interpretationBiological processesMultiple tissuesHidden layerNeural networkGenesMultiple networksLatent space modeling for human disease network with temporal variations: Analysis of medicare data
Zhu G, Wang R, Li R, Zhang S, Ma S, Qiao G, Mei H. Latent space modeling for human disease network with temporal variations: Analysis of medicare data. The Annals Of Applied Statistics 2026, 20: 364-384. DOI: 10.1214/25-aoas2121.Peer-Reviewed Original ResearchA Taxonomy for Assessing Real-World Targeted Cancer Therapy Options in the Context of Broad Genomic Profiling
Wang X, Long J, Rothen J, Huang S, Soulos P, Goldberg S, Robinson T, Ma S, Mamtani R, Presley C, Wang S, Kunst N, Gross C, Dinan M. A Taxonomy for Assessing Real-World Targeted Cancer Therapy Options in the Context of Broad Genomic Profiling. Journal Of The National Comprehensive Cancer Network 2026, 24: e257131. PMID: 41698347, PMCID: PMC13112471, DOI: 10.6004/jnccn.2025.7131.Peer-Reviewed Original ResearchThis study introduces Y-MATRIX, a taxonomy classifying genomic profiling results by clinical actionability, showing increased actionable findings in advanced lung cancer from 2017 to 2023.Integrating Omics and Pathological Imaging Data for Cancer Prognosis via a Deep Neural Network‐Based Cox Model
Li J, Ma S. Integrating Omics and Pathological Imaging Data for Cancer Prognosis via a Deep Neural Network‐Based Cox Model. Statistics In Medicine 2026, 45: e70435. PMID: 41641685, DOI: 10.1002/sim.70435.Peer-Reviewed Original Research
Clinical Trials
Current Trials
Molecular Markers of UV Exposure and Cancer Risk in Skin
IRB ID2000024848RoleSub InvestigatorPrimary Completion Date03/31/2024Recruiting Participants
News
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News
- July 23, 2025
Digging into data science at the 38th Annual New England Statistical Symposium
- May 06, 2025
Genetic Test Underused in Cancer Care
- January 07, 2025
Leadership Appointments Underscore Yale Biostatistics’ Global Strength in Research and Innovation
- October 24, 2024
New Analytics Center for Cardiovascular Medicine
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300 George Street
Academic Office
Ste 501
New Haven, CT 06511