Roberta De Vito

Roberta De Vito is an Assistant Professor at Brown University, jointly affiliated with the Data Science Institute and the Department of Biostatistics. She specializes in developing statistical methods for analyzing high-dimensional biological and epidemiological datasets, with a focus on Bayesian approaches and machine learning. Her research addresses critical health issues such as cancer prevention, depression in adolescents, and genomic data integration.

Education:

• B.S. in Statistics, University of Rome La Sapienza (Italy)
• Ph.D. in Statistics, University of Padua (Italy)
• Visiting Ph.D. Student at Harvard T.H. Chan School of Public Health
• Postdoctoral Fellow under Barbara Engelhardt at Princeton University

Research Interests:

Her work centers on statistical methodologies for big data, including multi-study factor analysis and Bayesian modeling. Current projects include:

• Methylation patterns linked to depression and early puberty
• Correlations between maternal and teen depression
• Autoantibody signatures in diseases like rheumatoid arthritis and post-COVID syndrome

Awards & Recognition:

• ISBA Travel Support (2018), ISBA Travel Award (2017)
• Best Poster Award at Reproducibility in Personalized Medicine (2016)
• WiML Travel Grant (2016)

Advising & Grants:

While no specific students are listed, her research has been supported by grants from institutions including Google (ISBA 2016) and the National Institutes of Health. She collaborates with labs focused on genomic and epidemiological data analysis.

Labs & Teams:

Active in Brown’s Data Science Initiative and collaborates with global institutions on projects involving multi-study data integration and statistical methodology development.