Paola Sebastiani, PhD

Director of the Biostatistics, Epidemiology, and Research Design (BERD) Center
Tufts CTSI; Institute for Clinical Research and Health Policy Studies

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.