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Research Expertise & Professional Interests Paola Sebastiani, Ph.D. joined the Department of Biostatistics in 2003 as an Associate Professor, after holding faculty positions in Italy, England and United States. She is author of more than 200 peer-reviewed publications in theoretical and methodological statistics, artificial intelligence, computational biology and genetics. She is statistical consultant for Circulation and also a regular reviewer for major journals in statistics and computer science, and serves on the program committee of several international conferences at the interface between statistics and artificial intelligence. When she joined the Department of Biostatistics at Boston University in 2003, Dr. Sebastiani had experience in interdisciplinary collaborations and a track record of developing novel methodologies in Bayesian statistics, machine learning, decision theory, graphical modeling and statistical experimental design. She leveraged this experience to develop a wide network of collaborations with investigators from the Bioinformatics program, the Genetics and Genomics program, and the Molecular and Translational Medicine Program. In these collaborations Dr. Sebastiani often introduced original solutions by developing 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. This work has been supported by the National Science Foundation and the National Institutes for Health and is currently funded by grants of which Dr. Sebastiani is Principal Investigator. Her contributions include, among others, a Bayesian model-based clustering procedure of temporal expression profiles (CAGED), a robust Bayesian approach to analyze differential gene expression using model averaging (BADGE), and novel methods for analysis of genetic data. Dr. Sebastiani was a pioneer in using a Bayesian network approach to model the genetic and phenotypic basis of 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. This model was successfully evaluated by independent investigators and has opened several new research areas in sickle cell disease. These results were the fruit of a long and productive collaboration with Dr. Steinberg to study the genetic basis of different clinical presentations of sickle cell disease. Dr. Sebastiani has also cultivated a strong and growing reputation as a biostatistician in the fields of gerontology, biology and epidemiology of human aging and longevity. She is the primary statistician of the BU site of the Long Life Family Study, and of the New England Centenarian Study directed by Dr. Thomas Perls. Dr. Sebastiani used an original Bayesian approach to verify the “compression of morbidity hypothesis” that had long been debated in the field of gerontology, developed a method for scoring sibships for familial longevity that can be used to enroll the most informative families in observational studies of human longevity, and introduced a novel Bayesian approach to model the genetic and phenotypic basis of exceptional human longevity. The analysis provides evidence that extreme human longevity is not due to absence of disease variants but to rare combinations of large numbers of common protective variants. Her current work focuses on the generation of molecular profiles to predict patterns of aging, and the biology of aging using a system-based approach.
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  • Machine Learning