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Paola Sebastiani, PhD
Director, Center for Quantitative Methods and Data Science(QM&DS),Institute for Clinical Research and Health Policy Studies (ICRHPS)
Director,Biostatistics, Epidemiology, and Research Design (BERD) Center, Tufts Clinical and Translational Science Institute (CTSI)
Department + Services
Institute for Clinical Research and Health Policy Studies, Tufts Clinical and Translational Science Institute (CTSI)
Research Focus Areas
Statistical methods for Bayesian modeling, design and analysis of observational studies, correlated data, statistical genetics and genomics, machine learning. Area of application: biology and epidemiology of aging and human longevity, HIV, sickle cell anemia.
2018 Research Award Excellence from the School of Public Health, Boston University
2017 ASA Fellow Elected fellow of the American Statistical Association.
2011 Teaching Award for teaching the course “Design and Analysis of Microarray Experiments" at Boston University School of Public Health
2009 Teaching Award for teaching the course “Bayesian Modeling in Biomedical Research and Public Health" at Boston University School of Public Health
2005 Teaching Award for teaching the course “Statistical Methods in Functional Genomics" that was ranked best course taught in the fall of 2004 at Boston University School of Public Health
1999 Honorary Mention Honorary mention at the Knowledge Discovery Cup, San Diego, CA for the work “Modeling customers' behavior using Bayesian Networks".
Dr. Sebastiani received training in Mathematics and Statistics in Italy and the United Kingdom, and held faculty positions at the University of Perugia - Italy, City University London, the Open University, Imperial College in the UK, the University of Massachusetts, Amherst, and Boston University, before joining Tufts Medical Center in 2020. Dr. Sebastiani has a strong track-record of teaching, mentoring, development of statistical methods, and leading interdisciplinary research projects. Dr. Sebastiani has introduced innovative Bayesian techniques for the analysis of genomic and genetic data and for the joint modeling of the genetic, genomic and phenotypic basis of complex traits. Examples include a Bayesian model-based clustering procedure of temporal expression profiles (CAGED), novel methods for analysis of genetic data, original approaches to analysis of clustered data, and to jointly analyze multiple biomarkers. Dr. Sebastiani was a pioneer in using a Bayesian network approach to model the genetic and phenotypic basis of the complications of sickle cell anemia. She developed the first network model for predicting stroke in patients with sickle cell anemia, and a network-based prognostic model that integrates sub-phenotypes of sickle cell anemia patients into a score of the overall severity of disease. Dr. Sebastiani is also a renowned biostatistician in the fields of biology and epidemiology of human aging and longevity. She is Co-PI of the Longevity Consortium, of the Long Life Family Study, primary statistician of the New England Centenarian Study directed by Dr. Thomas Perls, and multiple PI of a new multi-site project to generate multi-omics profiles of centenarians and their offspring. Dr. Sebastiani used an original Bayesian approach to test the compression of morbidity hypothesis that had long been debated in the field of gerontology, she developed a metric of familial longevity (the FLOSS score) that was used to enroll families in the Long Life Family Study, and she introduced a novel Bayesian approach to model the genetic and phenotypic basis of exceptional human longevity. She recently introduced a novel approach to discover biomarker signatures of healthy aging, and a network based approach to calculate the relative risk for longevity based on extended pedigrees. Her current research focuses on the genetics and epidemiology of extreme human longevity, analysis of rare genetic variants, and integrative analysis of multi-omics data.