Paola Sebastiani, Ph.D. (Twitter account: @P_Sebastiani) is a Professor in the Department of Biostatistics at Boston University School of Public Health and Adjunct Associate Professor in Bioinformatics, Boston University. She received a degree in Mathematics (Magna cum Laudem) from the University of Perugia, Italy, 1987, a Master degree (with Distinction) in Applied Stochastic Systems from the University College London, 1990, and a PhD in Statistics from the University of Rome, Italy, 1992. From 1990 to 1995 she wasresearcher at the Department of Statistics of the University of Perugia, Italy. Between 1995 and 2000 she held faculty positions at City University, London, the Open University, and Imperial College London, where she was Governor lecturer in the Mathematics Department. Between 2000 and 2003 she was Assistant Professor in the Department of Mathematics and Statistics, University of Massachusetts, Amherst, and joined the department of Biostatistics at Boston University in 2003 as Associate Professor. Her research interests include development and applications of Bayesian statistical methods in biomedical informatics, genomics and genetics. She developed novel methodologies in machine learning and artificial intelligence; decision theory; graphical modeling and statistical experimental design. Her contributions include a Bayesian model based clustering procedure of temporal expression profiles (CAGED), a robust Bayesian approach to analyze differential gene expression using model averaging (BADGE), novel methods for analysis of genetic data including a Bayesian estimator of allelic association, and a Bayesian network approach to model the genetic and phenotypic bases of complex traits. Her recent work focuses on the discovery of genetic modifers of sickle cell anemia and exceptional longevity and the generation of genetic risk models for public health. Dr Sebastiani was among the first to use a Bayesian network approach to model the genetic and phenotypic bases of complications of sickle cell anemia. She developed the rst complex model for predicting stroke in subjects with sickle cell anemia and a network-based prognostic model that integrates the phenotypic heterogeneity of sickle cell anemia patients into a score of the overall severity of disease.  In the aging field, Dr Sebastiani introduced a novel Bayesian approach to model the genetic and phenotypic bases of exceptional longevity. The model developed using this approach can be used to compute risk prediction based on the genetic pro le of subjects, and determine genetic signatures that correlate with different subphenotypes of exceptional longevity.