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Dr. Hongyu Zhao is the Ira V. Hiscock Professor of Biostatistics and Professor of Statistics and Data Science and Genetics, Chair of the Biostatistics Department and the Co-Director of Graduate Studies of the Inter-Departmental Program in Computational Biology and Bioinformatics at Yale University. He received his B.S. in Probability and Statistics from Peking University in 1990 and Ph.D. in Statistics from the University of California at Berkeley in 1995. His research interests are the applications of statistical methods in genetics, molecular biology, drug developments, and precision medicine.

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Genome Wide Association Studies: We are developing statistical methods to integrate diverse data types and prior biological knowledge to identify genes for common diseases and risk prediction models. We also develop methods to infer the genetic architecture of complex diseases and for risk predictions.
Single Cell Analysis: We are developing statistically robust and computationally efficient methods for single cell data with the objectives of inferring genetic regulation and signaling at the single cell level, and the identifications of cellular changes across different conditions.
Network Modeling: We are developing statistical methods to model biological networks under the general framework of Gaussian and other graphical models. Specific networks we are working on include gene expression regulatory networks, signaling networks, and eQTL networks.
Imaging Genetics: We focus on the analysis of data from several consortia to infer the impacts of genetics on imaging traits.

Wearable Device: We are developing methods to extract signals from wearable devices and then combine them with genetics data to infer the genetic basis of activity and sleeping traits.\\n
Cancer Genomics: We are developing statistical and computational methods to analyze cancer genomics data, e.g. microarrays and next generation sequencing, to identify cancer subtypes, driver mutations, and appropriate treatments for cancer patients.
Microbiome Analysis: We are developing modeling and analysis approaches for microbiome data generated from next generation sequencing data.
Proteomics: Our current focus is on targeted proteomics, such as Multiple Reaction Monitoring.

\",\"summary\":\"

Our research is driven by the need to analyze and interpret large and complex data sets in biomedical research. For example, in genome wide association studies involving thousands to hundreds of thousands of individuals, millions of DNA variants are analyzed for each person. Such data offer people the opportunity to identify variants affecting disease susceptibility and develop risk prediction models to facilitate disease prevention and treatment. There are many statistical challenges arising from the analysis of such data, including the very high dimensionality of the markers, the relatively weak signals, and the need to incorporate prior knowledge and other data sets in analysis. Other examples include the analyses of next generation sequence data, single cell data, image data, microbiome data, wearable device data, and electronic medical records, which present even greater statistical and computational challenges. Our group has been developing statistical methods to address these challenges, such as empirical Bayes methods to borrow information across different data sets, different generalizations of Gaussian graphical models for network inference, Markov random field models for spatial and temporal modeling, and general machine learning methods for high dimensional data.\\n

Specialized Terms: Statistical genomics and proteomics; Bioinformatics; Data integration; High dimensional data; Network and graphical models; Disease risk prediction; Microbiome; Cancer genomics; Single cell analysis; Imaging genetics; Wearable device; Electronic medical records\\n

\",\"researchInterests\":[\"Genetics\",\"Public Health\",\"Computational Biology\",\"Statistics\",\"Genomics\",\"Proteomics\",\"Biostatistics\",\"Single-Cell Analysis\",\"Microbiota\",\"Wearable Electronic Devices\"],\"healthInterests\":[\"Genetics, Genomics, Epigenetics\"],\"researchImagesGallery\":null},\"publications\":[{\"title\":\"Incorporating biological pathways via a Markov random field model in genome-wide association studies.\",\"citation\":\"Chen M, Cho J, Zhao H. Incorporating biological pathways via a Markov random field model in genome-wide association studies. PLoS Genetics 2011, 7:e1001353.\",\"year\":2011,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/21490723/\",\"text\":\"Incorporating biological pathways via a Markov random field model in genome-wide association studies.\"}},{\"title\":\"BAYESIAN HIERARCHICAL MODELING FOR SIGNALING PATHWAY INFERENCE FROM SINGLE CELL INTERVENTIONAL DATA.\",\"citation\":\"Luo R, Zhao H. BAYESIAN HIERARCHICAL MODELING FOR SIGNALING PATHWAY INFERENCE FROM SINGLE CELL INTERVENTIONAL DATA. The Annals Of Applied Statistics 2011, 5:725-745.\",\"year\":2011,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/22162986/\",\"text\":\"BAYESIAN HIERARCHICAL MODELING FOR SIGNALING PATHWAY INFERENCE FROM SINGLE CELL INTERVENTIONAL DATA.\"}},{\"title\":\"Sparse Estimation of Conditional Graphical Models With Application to Gene Networks.\",\"citation\":\"Li B, Chuns H, Zhao H. Sparse Estimation of Conditional Graphical Models With Application to Gene Networks. Journal Of The American Statistical Association 2012, 107:152-167.\",\"year\":2012,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/24574574/\",\"text\":\"Sparse Estimation of Conditional Graphical Models With Application to Gene Networks.\"}},{\"title\":\"iFad: an integrative factor analysis model for drug-pathway association inference.\",\"citation\":\"Ma H, Zhao H. iFad: an integrative factor analysis model for drug-pathway association inference. Bioinformatics (Oxford, England) 2012, 28:1911-8.\",\"year\":2012,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/22581178/\",\"text\":\"iFad: an integrative factor analysis model for drug-pathway association inference.\"}},{\"title\":\"Sparse principal component analysis by choice of norm.\",\"citation\":\"Qi X, Luo R, Zhao H. Sparse principal component analysis by choice of norm. Journal Of Multivariate Analysis 2013, 114:127-160.\",\"year\":2013,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/23524453/\",\"text\":\"Sparse principal component analysis by choice of norm.\"}},{\"title\":\"GPA: a statistical approach to prioritizing GWAS results by integrating pleiotropy and annotation.\",\"citation\":\"Chung D, Yang C, Li C, Gelernter J, Zhao H. GPA: a statistical approach to prioritizing GWAS results by integrating pleiotropy and annotation. PLoS Genetics 2014, 10:e1004787.\",\"year\":2014,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/25393678/\",\"text\":\"GPA: a statistical approach to prioritizing GWAS results by integrating pleiotropy and annotation.\"}},{\"title\":\"On an Additive Semigraphoid Model for Statistical Networks With Application to Pathway Analysis.\",\"citation\":\"Li B, Chun H, Zhao H. On an Additive Semigraphoid Model for Statistical Networks With Application to Pathway Analysis. Journal Of The American Statistical Association 2014, 109:1188-1204.\",\"year\":2014,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/26401064/\",\"text\":\"On an Additive Semigraphoid Model for Statistical Networks With Application to Pathway Analysis.\"}},{\"title\":\"Guilt by rewiring: gene prioritization through network rewiring in genome wide association studies.\",\"citation\":\"Hou L, Chen M, Zhang CK, Cho J, Zhao H. Guilt by rewiring: gene prioritization through network rewiring in genome wide association studies. Human Molecular Genetics 2014, 23:2780-90.\",\"year\":2014,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/24381306/\",\"text\":\"Guilt by rewiring: gene prioritization through network rewiring in genome wide association studies.\"}},{\"title\":\"Improving genetic risk prediction by leveraging pleiotropy.\",\"citation\":\"Li C, Yang C, Gelernter J, Zhao H. Improving genetic risk prediction by leveraging pleiotropy. Human Genetics 2014, 133:639-50.\",\"year\":2014,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/24337655/\",\"text\":\"Improving genetic risk prediction by leveraging pleiotropy.\"}},{\"title\":\"A statistical framework to predict functional non-coding regions in the human genome through integrated analysis of annotation data.\",\"citation\":\"Lu Q, Hu Y, Sun J, Cheng Y, Cheung KH, Zhao H. A statistical framework to predict functional non-coding regions in the human genome through integrated analysis of annotation data. Scientific Reports 2015, 5:10576.\",\"year\":2015,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/26015273/\",\"text\":\"A statistical framework to predict functional non-coding regions in the human genome through integrated analysis of annotation data.\"}},{\"title\":\"Pervasive pleiotropy between psychiatric disorders and immune disorders revealed by integrative analysis of multiple GWAS.\",\"citation\":\"Wang Q, Yang C, Gelernter J, Zhao H. Pervasive pleiotropy between psychiatric disorders and immune disorders revealed by integrative analysis of multiple GWAS. Human Genetics 2015, 134:1195-209.\",\"year\":2015,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/26340901/\",\"text\":\"Pervasive pleiotropy between psychiatric disorders and immune disorders revealed by integrative analysis of multiple GWAS.\"}},{\"title\":\"A MARKOV RANDOM FIELD-BASED APPROACH TO CHARACTERIZING HUMAN BRAIN DEVELOPMENT USING SPATIAL-TEMPORAL TRANSCRIPTOME DATA.\",\"citation\":\"Lin Z, Sanders SJ, Li M, Sestan N, State MW, Zhao H. A MARKOV RANDOM FIELD-BASED APPROACH TO CHARACTERIZING HUMAN BRAIN DEVELOPMENT USING SPATIAL-TEMPORAL TRANSCRIPTOME DATA. The Annals Of Applied Statistics 2015, 9:429-451.\",\"year\":2015,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/26877824/\",\"text\":\"A MARKOV RANDOM FIELD-BASED APPROACH TO CHARACTERIZING HUMAN BRAIN DEVELOPMENT USING SPATIAL-TEMPORAL TRANSCRIPTOME DATA.\"}},{\"title\":\"Integrative Tissue-Specific Functional Annotations in the Human Genome Provide Novel Insights on Many Complex Traits and Improve Signal Prioritization in Genome Wide Association Studies.\",\"citation\":\"Lu Q, Powles RL, Wang Q, He BJ, Zhao H. Integrative Tissue-Specific Functional Annotations in the Human Genome Provide Novel Insights on Many Complex Traits and Improve Signal Prioritization in Genome Wide Association Studies. PLoS Genetics 2016, 12:e1005947.\",\"year\":2016,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/27058395/\",\"text\":\"Integrative Tissue-Specific Functional Annotations in the Human Genome Provide Novel Insights on Many Complex Traits and Improve Signal Prioritization in Genome Wide Association Studies.\"}},{\"title\":\"On an additive partial correlation operator and nonparametric estimation of graphical models.\",\"citation\":\"Lee KY, Li B, Zhao H. On an additive partial correlation operator and nonparametric estimation of graphical models. Biometrika 2016, 103:513-530.\",\"year\":2016,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/29422689/\",\"text\":\"On an additive partial correlation operator and nonparametric estimation of graphical models.\"}},{\"title\":\"CCor: A whole genome network-based similarity measure between two genes.\",\"citation\":\"Hu Y, Zhao H. CCor: A whole genome network-based similarity measure between two genes. Biometrics 2016, 72:1216-1225.\",\"year\":2016,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/26953524/\",\"text\":\"CCor: A whole genome network-based similarity measure between two genes.\"}},{\"title\":\"On high-dimensional misspecified mixed model analysis in genome-wide association study\",\"citation\":\"J. Jiang, C. Li, D. Paul, C. Yang, H. Zhao (2016) On high-dimensional misspecified mixed model analysis in genome-wide association study. Annals of Statistics, 44: 2127–2160.\",\"year\":2016,\"link\":null},{\"title\":\"Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction.\",\"citation\":\"Hu Y, Lu Q, Liu W, Zhang Y, Li M, Zhao H. Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction. PLoS Genetics 2017, 13:e1006836.\",\"year\":2017,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/28598966/\",\"text\":\"Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction.\"}},{\"title\":\"A Powerful Approach to Estimating Annotation-Stratified Genetic Covariance via GWAS Summary Statistics.\",\"citation\":\"Lu Q, Li B, Ou D, Erlendsdottir M, Powles RL, Jiang T, Hu Y, Chang D, Jin C, Dai W, He Q, Liu Z, Mukherjee S, Crane PK, Zhao H. A Powerful Approach to Estimating Annotation-Stratified Genetic Covariance via GWAS Summary Statistics. American Journal Of Human Genetics 2017, 101:939-964.\",\"year\":2017,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/29220677/\",\"text\":\"A Powerful Approach to Estimating Annotation-Stratified Genetic Covariance via GWAS Summary Statistics.\"}},{\"title\":\"Leveraging functional annotations in genetic risk prediction for human complex diseases.\",\"citation\":\"Hu Y, Lu Q, Powles R, Yao X, Yang C, Fang F, Xu X, Zhao H. Leveraging functional annotations in genetic risk prediction for human complex diseases. PLoS Computational Biology 2017, 13:e1005589.\",\"year\":2017,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/28594818/\",\"text\":\"Leveraging functional annotations in genetic risk prediction for human complex diseases.\"}},{\"title\":\"Systematic tissue-specific functional annotation of the human genome highlights immune-related DNA elements for late-onset Alzheimer's disease.\",\"citation\":\"Lu Q, Powles RL, Abdallah S, Ou D, Wang Q, Hu Y, Lu Y, Liu W, Li B, Mukherjee S, Crane PK, Zhao H. Systematic tissue-specific functional annotation of the human genome highlights immune-related DNA elements for late-onset Alzheimer's disease. PLoS Genetics 2017, 13:e1006933.\",\"year\":2017,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/28742084/\",\"text\":\"Systematic tissue-specific functional annotation of the human genome highlights immune-related DNA elements for late-onset Alzheimer's disease.\"}},{\"title\":\"Spectral clustering based on learning similarity matrix.\",\"citation\":\"Park S, Zhao H, Birol I. Spectral clustering based on learning similarity matrix. Bioinformatics (Oxford, England) 2018, 34:2069-2076.\",\"year\":2018,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/29432517/\",\"text\":\"Spectral clustering based on learning similarity matrix.\"}},{\"title\":\"A Dirichlet-tree multinomial regression model for associating dietary nutrients with gut microorganisms.\",\"citation\":\"Wang T, Zhao H. A Dirichlet-tree multinomial regression model for associating dietary nutrients with gut microorganisms. Biometrics 2017, 73:792-801.\",\"year\":2017,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/28112797/\",\"text\":\"A Dirichlet-tree multinomial regression model for associating dietary nutrients with gut microorganisms.\"}},{\"title\":\"On joint estimation of Gaussian graphical models for spatial and temporal data.\",\"citation\":\"Lin Z, Wang T, Yang C, Zhao H. On joint estimation of Gaussian graphical models for spatial and temporal data. Biometrics 2017, 73:769-779.\",\"year\":2017,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/28099997/\",\"text\":\"On joint estimation of Gaussian graphical models for spatial and temporal data.\"}},{\"title\":\"Spatiotemporal transcriptomic divergence across human and macaque brain development.\",\"citation\":\"Zhu Y, Sousa AMM, Gao T, Skarica M, Li M, Santpere G, Esteller-Cucala P, Juan D, Ferrández-Peral L, Gulden FO, Yang M, Miller DJ, Marques-Bonet T, Imamura Kawasawa Y, Zhao H, Sestan N. Spatiotemporal transcriptomic divergence across human and macaque brain development. Science (New York, N.Y.) 2018, 362.\",\"year\":2018,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/30545855/\",\"text\":\"Spatiotemporal transcriptomic divergence across human and macaque brain development.\"}},{\"title\":\"A statistical framework for cross-tissue transcriptome-wide association analysis.\",\"citation\":\"Hu Y, Li M, Lu Q, Weng H, Wang J, Zekavat SM, Yu Z, Li B, Gu J, Muchnik S, Shi Y, Kunkle BW, Mukherjee S, Natarajan P, Naj A, Kuzma A, Zhao Y, Crane PK, Lu H, Zhao H. A statistical framework for cross-tissue transcriptome-wide association analysis. Nature Genetics 2019, 51:568-576.\",\"year\":2019,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/30804563/\",\"text\":\"A statistical framework for cross-tissue transcriptome-wide association analysis.\"}},{\"title\":\"Prediction analysis for microbiome sequencing data.\",\"citation\":\"Wang T, Yang C, Zhao H. Prediction analysis for microbiome sequencing data. Biometrics 2019, 75:875-884.\",\"year\":2019,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/30994187/\",\"text\":\"Prediction analysis for microbiome sequencing data.\"}},{\"title\":\"NITUMID: Nonnegative matrix factorization-based Immune-TUmor MIcroenvironment Deconvolution.\",\"citation\":\"Tang D, Park S, Zhao H. NITUMID: Nonnegative matrix factorization-based Immune-TUmor MIcroenvironment Deconvolution. Bioinformatics (Oxford, England) 2020, 36:1344-1350.\",\"year\":2020,\"link\":{\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/31593244/\",\"text\":\"NITUMID: Nonnegative matrix factorization-based Immune-TUmor MIcroenvironment Deconvolution.\"}}],\"clinicalTrials\":[{\"url\":\"/ycci/trial/1246/\",\"title\":\"Pathogenesis of Youth and Type 2 Diabetes and Prediabetes\",\"conditions\":[\"Children's Health\",\"Diabetes Mellitus - Type 2\",\"Diseases of the Endocrine System\"]}],\"coauthors\":[]},\"patientCareTab\":null,\"newsTab\":{\"searchUrl\":\"/ysm/search/?entityType=Article&profilePageName=hongyu_zhao\",\"news\":[{\"id\":\"22951\",\"bylineDate\":\"2020-03-04T21:38:32-05:00\",\"title\":\"Geneticist Sidi Chen Receives Large Award Which Will Further His Breast Cancer Work\",\"summary\":\"This Department of Defense award goes to the “best and brightest in their fields,” and recognizes “creative and innovative individuals.” Investigators are chosen for their ability “to go beyond conventional thinking” in their respective areas of expertise.\",\"thumbnail\":{\"url\":\"https://ysm-res.cloudinary.com/image/upload/v1/yms/prod/05fc953f-c3ff-43ff-8ea4-36a91e959df7\",\"altText\":null,\"width\":7360,\"height\":4912,\"imageVariants\":[{\"dimension\":\"Square\",\"url\":\"https://ysm-res.cloudinary.com/image/upload/v1/yms/prod/f9784bf7-fa35-42a8-9873-5a7ae5c4721f\",\"width\":4912,\"height\":4912},{\"dimension\":\"Rectangle\",\"url\":\"https://ysm-res.cloudinary.com/image/upload/v1/yms/prod/995012b2-0a7f-45e7-b602-612c257bf043\",\"width\":6064,\"height\":3256}]},\"url\":\"/ysm/news-article/22951/\",\"isFeatured\":false},{\"id\":\"22237\",\"bylineDate\":\"2020-01-07T05:00:00-05:00\",\"title\":\"YSPH Researcher Receives Top Prize in Statistics\",\"summary\":\"Hongyu Zhao was recently presented the Pao-Lu Hsu Prize at the 11th International Chinese Statistical Association (ICSA) conference in Hangzhou, China.\",\"thumbnail\":{\"url\":\"https://ysm-res.cloudinary.com/image/upload/v1/yms/prod/2105bd89-3ac9-4601-9c65-7b5755b39558\",\"altText\":null,\"width\":3407,\"height\":2119,\"imageVariants\":[{\"dimension\":\"Square\",\"url\":\"https://ysm-res.cloudinary.com/image/upload/v1/yms/prod/1948d0b6-7ac6-4050-b296-4f3dce5d7324\",\"width\":2119,\"height\":2119},{\"dimension\":\"Rectangle\",\"url\":\"https://ysm-res.cloudinary.com/image/upload/v1/yms/prod/74954b61-227d-4f5f-9f8f-ca421be13367\",\"width\":3407,\"height\":1829}]},\"url\":\"/ysm/news-article/22237/\",\"isFeatured\":false},{\"id\":\"21247\",\"bylineDate\":\"2019-09-17T12:30:00-04:00\",\"title\":\"The Genetic Origins of Sex Differences in Disease\",\"summary\":\"Women's Health Research at Yale is launching a genome-wide association study to uncover the genetic origins of sex differences in disease.\",\"thumbnail\":{\"url\":\"https://ysm-res.cloudinary.com/image/upload/v1/yms/prod/d91a827c-b2cb-47d6-8dce-16d8942d5290\",\"altText\":null,\"width\":3456,\"height\":2304,\"imageVariants\":[{\"dimension\":\"Square\",\"url\":\"https://ysm-res.cloudinary.com/image/upload/v1/yms/prod/d848b7c1-74f5-43ee-9631-fd429af76e7f\",\"width\":2304,\"height\":2304},{\"dimension\":\"Rectangle\",\"url\":\"https://ysm-res.cloudinary.com/image/upload/v1/yms/prod/0084cd1c-11ac-4690-a523-d13825122e87\",\"width\":3358,\"height\":2304}]},\"url\":\"/ysm/news-article/21247/\",\"isFeatured\":false},{\"id\":\"20293\",\"bylineDate\":\"2019-05-23T04:00:00-04:00\",\"title\":\"WHRY funds studies on pain relief, cannabis, and genetic origins of sex differences in disease\",\"summary\":\"WHRY announces funding for first-of-their-kind studies on pain relief, cannabis, and the genetic basis of sex differences across a broad range of ailments, including Alzheimer’s disease and heart disease.\",\"thumbnail\":{\"url\":\"https://ysm-res.cloudinary.com/image/upload/v1/yms/prod/62f6882c-5292-48d8-b7ae-78e64b2ae505\",\"altText\":null,\"width\":5538,\"height\":3692,\"imageVariants\":[{\"dimension\":\"Square\",\"url\":\"https://ysm-res.cloudinary.com/image/upload/v1/yms/prod/d4be2a98-8175-479c-b709-70889f1f9916\",\"width\":3692,\"height\":3692},{\"dimension\":\"Rectangle\",\"url\":\"https://ysm-res.cloudinary.com/image/upload/v1/yms/prod/8635ae3f-5dcb-4d9e-9de5-4f842c240194\",\"width\":5381,\"height\":3692}]},\"url\":\"https://news.yale.edu/2019/05/23/whry-funds-studies-pain-relief-cannabis-and-sex-differences-disease\",\"isFeatured\":false},{\"id\":\"20298\",\"bylineDate\":\"2019-05-23T04:00:00-04:00\",\"title\":\"Women’s Health Research at Yale funds studies on pain relief, cannabis, and genetic origins of sex differences in disease\",\"summary\":\"Women’s Health Research at Yale (WHRY) today announced funding for first-of-their-kind studies on pain relief, cannabis, and the genetic basis of sex differences across a broad range of ailments, including Alzheimer’s disease and heart disease.\",\"thumbnail\":{\"url\":\"https://ysm-res.cloudinary.com/image/upload/v1/yms/prod/865f3c5d-ad87-45b5-b070-0d18d614acd6\",\"altText\":null,\"width\":1022,\"height\":594,\"imageVariants\":[{\"dimension\":\"Square\",\"url\":\"https://ysm-res.cloudinary.com/image/upload/v1/yms/prod/69e63b84-c540-410f-9a50-94ee73fb4413\",\"width\":594,\"height\":594},{\"dimension\":\"Rectangle\",\"url\":\"https://ysm-res.cloudinary.com/image/upload/v1/yms/prod/4f542740-75f7-4340-b067-7e2938b275b2\",\"width\":865,\"height\":594}]},\"url\":\"/ysm/news-article/20298/\",\"isFeatured\":false},{\"id\":\"19859\",\"bylineDate\":\"2019-04-04T04:00:00-04:00\",\"title\":\"New Center to Harness Burgeoning Data\",\"summary\":\"The Center for Biomedical Data Science (CBDS) is one part clearinghouse for the newest data-related ideas; one part incubator for ways to educate students, trainees, and faculty on what data can offer; and one part an innovator of data techniques to advance biomedical science and data science. As CBDS matures, additional pursuits are sure to follow.\",\"thumbnail\":{\"url\":\"https://ysm-res.cloudinary.com/image/upload/v1/yms/prod/950fe017-595a-41fa-9ea2-cb587668b0e2\",\"altText\":null,\"width\":5000,\"height\":3333,\"imageVariants\":[{\"dimension\":\"Square\",\"url\":\"https://ysm-res.cloudinary.com/image/upload/v1/yms/prod/a6443822-4b99-4623-9b91-78f00d79868a\",\"width\":3333,\"height\":3333},{\"dimension\":\"Rectangle\",\"url\":\"https://ysm-res.cloudinary.com/image/upload/v1/yms/prod/091d71a5-3b3b-410b-b111-c9277c186374\",\"width\":4022,\"height\":2759}]},\"url\":\"https://medicine.yale.edu/news/medicineatyale/new-center-to-harness-burgeoning-data.aspx\",\"isFeatured\":false},{\"id\":\"19630\",\"bylineDate\":\"2019-03-14T04:00:00-04:00\",\"title\":\"Novel YSPH Framework Helps Identify Genes Associated with Disease\",\"summary\":\"A powerful analytical tool, known as UTMOST, developed by Hongyu Zhao, Ph.D., the Ira V. Hiscock Professor of Biostatistics at the Yale School of Public Health, and colleagues could allow researchers to design therapeutic drugs that more effectively combat disease.\",\"thumbnail\":{\"url\":\"https://ysm-res.cloudinary.com/image/upload/v1/yms/prod/0a60d1dc-fac7-40e9-adee-de0d36d73eee\",\"altText\":null,\"width\":1800,\"height\":1172,\"imageVariants\":[{\"dimension\":\"Square\",\"url\":\"https://ysm-res.cloudinary.com/image/upload/v1/yms/prod/13e85b40-2705-4f05-978d-d17bb4f889c5\",\"width\":1172,\"height\":1172},{\"dimension\":\"Rectangle\",\"url\":\"https://ysm-res.cloudinary.com/image/upload/v1/yms/prod/2a21aa71-a7ea-4000-ae66-897e8447d6a2\",\"width\":1708,\"height\":1172}]},\"url\":\"/ysm/news-article/19630/\",\"isFeatured\":false},{\"id\":\"19432\",\"bylineDate\":\"2019-02-13T05:00:00-05:00\",\"title\":\"New Health Informatics Division Launched at Yale School of Public Health\",\"summary\":\"The Department of Biostatistics has launched a Health Informatics Division at the Yale School of Public Health along with a new M.S. track in health informatics.\",\"thumbnail\":{\"url\":\"https://ysm-res.cloudinary.com/image/upload/v1/yms/prod/a51b55fa-ab94-471a-b1b0-315e1684c655\",\"altText\":null,\"width\":1606,\"height\":1001,\"imageVariants\":[{\"dimension\":\"Square\",\"url\":\"https://ysm-res.cloudinary.com/image/upload/v1/yms/prod/6fbb0345-bdc6-4b77-bc7b-38bdc4aed679\",\"width\":1001,\"height\":1001},{\"dimension\":\"Rectangle\",\"url\":\"https://ysm-res.cloudinary.com/image/upload/v1/yms/prod/21c6c0d6-148f-45f9-b446-e535d152bdb3\",\"width\":1458,\"height\":1001}]},\"url\":\"/ysm/news-article/19432/\",\"isFeatured\":false}]},\"locationsTab\":{\"addresses\":[{\"id\":2872,\"address\":{\"id\":348,\"officeName\":null,\"name\":null,\"addressLine1\":\"300 George Street\",\"addressLine2\":\"Ste 503\",\"city\":\"New Haven\",\"region\":\"CT\",\"country\":\"United States\",\"postalCode\":\"06511\"},\"geoPoint\":{\"latitude\":41.304974,\"longitude\":-72.931337},\"phones\":[],\"activeHours\":null,\"website\":null}]},\"metaData\":{\"title\":\"Hongyu Zhao, PhD\",\"description\":\"Dr. Hongyu Zhao is the Ira V. Hiscock Professor of Biostatistics and Professor of Statistics and Data Science and Genetics, Chair of the Biostatistics Department and the Co-Director of Graduate Studies of the Inter-Departmental Program in Computational Biology and Bioinformatics at Yale University. He received his B.S. in Probability and Statistics from Peking University in 1990 and Ph.D. in Statistics from the University of California at Berkeley in 1995. His research interests are the applications of statistical methods in genetics, molecular biology, drug developments, and precision medicine.Some of his recent projects include large scale genome wide studies to identify genetic variants underlying complex diseases, genetic risk prediction, biological network modeling and analysis, disease biomarker identification, genome annotation, cancer genomics, microbiome analysis, single cell analysis, and image analysis. He has published over 530 articles in statistics, human genetics, bioinformatics, and proteomics, and edited two books on human genetics analysis and statistical genomics. He has trained over 80 doctoral and post-doctoral students, many of whom are holding tenured or tenure-track positions at major universities in the states and overseas.Dr. Zhao is a Co-Editor of the Journal of the American Statistical Association Theory and Methods, and serves on the editorial boards of several leading statistical and genetics journals. He was the recipient of the Mortimer Spiegelman Award for a top statistician in health statistics under the age of 40 awarded by the American Public Health Association and the Pao-Lu Hsu Award from the International Chinese Statistical Association. His research has also been recognized by the Evelyn Fix Memorial Medal and Citation by UC Berkeley, a Basil O'Connor Starter Scholar Award by the March of Dimes Foundation, election to the fellowship of the American Association for the Advancement of Science, the American Statistical Association, and the Institute of Mathematical Statistics.\",\"imageMetaData\":{\"alt\":\"\",\"width\":420,\"height\":630,\"url\":\"https://ysm-res.cloudinary.com/image/upload/c_limit,f_auto,h_630,q_auto,w_1200/v1/yms/prod/c21896d5-fb8e-41b3-9214-468fd51af7a8\",\"secureUrl\":\"https://ysm-res.cloudinary.com/image/upload/c_limit,f_auto,h_630,q_auto,w_1200/v1/yms/prod/c21896d5-fb8e-41b3-9214-468fd51af7a8\"},\"videoMetaData\":null,\"audioMetaData\":null,\"author\":null,\"pageType\":null,\"publishDate\":null,\"canonicalUrl\":\"/ysm/profile/hongyu_zhao/\"},\"structuredData\":[{\"schemaName\":\"Person\",\"jsonData\":\"{\\\"@type\\\":\\\"Person\\\",\\\"mainEntityOfPage\\\":{\\\"@type\\\":\\\"WebPage\\\",\\\"@id\\\":\\\"https://medicine.yale.edu/ysm/profile/hongyu_zhao/\\\",\\\"@context\\\":\\\"http://schema.org\\\"},\\\"name\\\":\\\"Hongyu Zhao\\\",\\\"description\\\":\\\"Dr. Hongyu Zhao is the Ira V. Hiscock Professor of Biostatistics and Professor of Statistics and Data Science and Genetics, Chair of the Biostatistics Department and the Co-Director of Graduate Studies of the Inter-Departmental Program in Computational Biology and Bioinformatics at Yale University. He received his B.S. in Probability and Statistics from Peking University in 1990 and Ph.D. in Statistics from the University of California at Berkeley in 1995. His research interests are the applications of statistical methods in genetics, molecular biology, drug developments, and precision medicine.Some of his recent projects include large scale genome wide studies to identify genetic variants underlying complex diseases, genetic risk prediction, biological network modeling and analysis, disease biomarker identification, genome annotation, cancer genomics, microbiome analysis, single cell analysis, and image analysis. He has published over 530 articles in statistics, human genetics, bioinformatics, and proteomics, and edited two books on human genetics analysis and statistical genomics. He has trained over 80 doctoral and post-doctoral students, many of whom are holding tenured or tenure-track positions at major universities in the states and overseas.Dr. Zhao is a Co-Editor of the Journal of the American Statistical Association Theory and Methods, and serves on the editorial boards of several leading statistical and genetics journals. He was the recipient of the Mortimer Spiegelman Award for a top statistician in health statistics under the age of 40 awarded by the American Public Health Association and the Pao-Lu Hsu Award from the International Chinese Statistical Association. 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