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Giovanni Parmigiani, PhD



  • Professor of Biostatistics

Contact Information

  • Office Phone Number617-632-5323
  • Fax617-632-2444


Giovanni Parmigiani, PhD, is a professor of Biostatistics at Harvard TH Chan School of Public Health and Dana-Farber Cancer Institute and Associate Director for Population Sciences at the Dana-Farber/Harvard Cancer Center. He received his undergraduate degree in economics and social sciences at Università L. Bocconi, and a Masters and PhD in statistics from Carnegie Mellon University. He has held faculty positions at Carnegie Mellon, Duke and Johns Hopkins, before joining the faculty at the Harvard TH Chan School of Public Health in 2009 and becoming the Chair of the Department of Biostatistics and Computational Biology at the Dana-Farber Cancer Institute.

Dr. Parmigiani is the recipient of numerous awards for his contributions to both science and teaching, including the Advising, Mentoring, and Teaching Recognition Award from the Johns Hopkins School of Public Health Student Assembly. He was named a Fellow of the American Statistical Association in 1999. While completing graduate studies at Carnegie Mellon, he received the Leonard J. Savage Dissertation Prize. His 2009 book on “Decision Theory” received the DeGroot prize. Dr. Parmigiani’s work has been published in the Journal of the American Medical Association, Science, Cancer Research, the Journal of the American Statistical Association, the Journal of Clinical Oncology, and the American Journal of Human Genetics.

Recent Awards:

  • Casty Award 2020
  • Junior Faculty Mentoring Award, Harvard T.H. Chan School of Public Health 2016
  • DeGroot Prize for Decision Theory, by Giovanni Parmigiani and Lurdes Y T Inoue, with contributions by Hedibert Freitas Lopes 2009
  • Advising, Mentoring, and Teaching Recognition Award. Johns Hopkins School of Public Health Student Assembly 2002


Professor of Biostatistics at Harvard TH Chan School of Public Health and Dana Farber Cancer Institute. Professor Parmigiani is a statistician whose work creates statistical tools for understanding cancer data, with particular focus on genetic epidemiology and genomics. For example he uses Bayesian modeling and machine learning concepts for predicting who is at risk of carrying genetic variants that confer susceptibility to cancer.  For another example, he is interested in addressing the challenges of cross-study replication of predictions by constructing predictors that learn replicability from multiple studies. His overarching goals are to increase the rigor end efficiency with which we leverage the vast and complex information generated in today’s cancer research; and to foster the use of data sciences as a common thread to facilitate interactions between fields and academic cultures.


Dana-Farber Cancer Institute
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