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Emanuele Mazzola, PhD


  • Research Associate

Contact Information

  • Office Phone Number617-582-7614


I received my M.S. in Mathematics from the Università degli Studi di Milano, Italy in 2002 with a thesis on spatial statistics. 
I then received a M.S. in Biostatistics from the Università degli Studi di Milano-Bicocca in 2005 with a thesis on Generalized linear models in ecotoxicology.
In 2010 I received my Ph.D. in Statistics from the Università Commerciale L.Bocconi, Milano, Italy with a thesis on Lévy processes.
I have been a postdoctoral fellow under the mentorship of Prof. Giovanni Parmigiani since November 2010 at the Department of Biostatistics and Computational Biology at Dana-Farber Cancer Institute. 
I became a Research Associate in the same department in November 2012.


1. Risk prediction of breast cancer following a diagnosis of precancerous events.
2. Microsimulation models: estimation and calibration via MCMC techniques.
3. Methodologies for identifying diagnostic and prognostic variables.

1. Since November 2010 I have joined the BayesMendel group led by Prof.Giovanni Parmigiani, and focused on the expansion of the BRCAPRO risk prediction model to include DCIS and contralateral breast cancer. 
I have an ongoing  collaboration with Dr.Kevin Hughes’s team at MGH for the development of a risk prediction model  targeted to women diagnosed with atypical hyperplasia of the breast.

2. Microsimulation models formally represent the natural history of cancer at individual level. This work is based on MCMC techniques  both to estimate the age of onset of preclinical asymptomatic cancer (deconvolution problem), and to suitably choose model parameters consistent with a known calibration dataset (calibration problem).

3. This work proposes an exploratory methodology on high dimensional data to identify variables associated with a phenotype, where there could be differential association across subgroups of subjects. 

Such relationships can often be present in genomic datasets, where markers may either be relevant for identifying subgroups or may be associated with the phenotype of interest. 
The proposed method addresses this problem by combining ideas of model-based clustering and Bayesian CART.

4. I am collaborating with Dr.Donna Neuberg as a biostatistician with the NeuroStemCell workgroup at Dana-Farber Cancer Institute, on a project involving image analysis to detect niches for pilocytic astrocytoma. 

I am also participating in the analysis of clinical trials data on Secondary AML.

In November 2012 I have joined the biostatistics team at CFAR (Harvard University Center for Aids Research) led by Prof.Rebecca Gelman.

I have an ongoing collaboration with Dr.Paul Catalano for the development of educational materials for in-person and online sessions to be done in conjunction with the Dana-Farber Cancer Institute's Clinical Trials Education Office.

Research Departments:


Dana-Farber Cancer Institute
450 Brookline Avenue
CLS 11075
Boston, MA 02215
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