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 inherited cancer understand their risk and make informed decisions. He uses Bayesian modeling and machine learning concepts to predict who is at risk of carrying genetic variants, and to integrate literature-based and other information about the effects of mutations. Visit the BayesMendel page to find out more about this line of investigation.
Throughout his research activities, his broad goals are 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 today’s cancer research. He strives 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 and training young(er) scientists in interdisciplinary settings.
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. His home is in the Department of Data Science at Dana-Farber Cancer Institute, of which he has been the Chairman from 2009 to 2018. He has also been the faculty Leader of DF/HCC's Biostatistics and Computational Biology Program (now Cancer Data Sciences Program) from 2009 to 2015.