Giovanni Parmigiani’s research investigates statistical principles and tools, often with a focus on understanding cancer data. For example, he is currently interested in addressing the challenges of cross-study replication of predictions, by constructing predictors that learn replicability from being trained on multiple studies at once. He also has a long term interest in helping families who are particularly susceptible to inheritedÂ cancer understand their risk and make informed decisions. He uses Bayesian modeling and machine learning concepts to predictÂ who is at risk of carrying genetic variants, and to integrate literature-based and other information about the effects of mutations. Visit theÂ BayesMendelÂ page to find out more about this line of investigation.
Throughout his researchÂ activities, his broad goals areÂ to find innovative ways to use data science and data technologies to fuel cancer prevention and early detection and, methodologically, to increase the rigor end efficiency with which we leverage the vast and complex information generated in todayâ€™s cancer research. He strivesÂ to foster the use of data sciences as a common thread to facilitate interactions between fields and academic cultures, and has a passion for mentoring andÂ training young(er) scientists in interdisciplinary settings.
Since joining Harvard in 2009 he has taken on several leadership roles: he is the Associate Director for Population Sciences of the multi-institutional Dana-Farber / Harvard Cancer Center (DF/HCC), and is the director of the postdoctoral training grant in Quantitative Sciences for Cancer Research at the Harvard T.H. Chan School of Public Health, where he is a Professor. He has been the Chairman of the Department of Biostatistics & Computational Biology at Dana-Farber Cancer Institute from 2009 to 2018, and the Leader of the DF/HCC Biostatistics and Computational Biology Program (now Cancer Data Sciences Program) from 2009 to 2015.