Assistant Professor


About Me

Hi! Welcome to my webpage. I am Roberta De Vito, Assistant Professor at Brown University in the department of Biostatistics and at the Data Science Institute. Previously, I was a postdoctoral fellow at Princeton University in the Department of Computer Science, advised by Barbara Engelhardt. I was a former visiting PhD Student at Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health under the supervision of Giovanni Parmigiani and Lorenzo Trippa. I graduated at University of Padua in Statistical Science in 2016 advised by Ruggero Bellio.


The focus of my research is to develop statistical modeling for the analysis of big and high-throughput data, such as epidemiological and genomic data analysis. In this big picture, I use Bayesian approach and machine learning techniques to investigate biological issues, such as cancer, and depression. 

My research interests are:

Currently I am working in the following applications:

•Methylation associated with depression and early puberty
•Correlation between Mother depression and teen depression


Preprints in review:

  • De Vito, R., Bellio, R., Trippa, L., & Parmigiani, G. (2018). Bayesian Multi-study Factor Analysis for High-throughput Biological Data. arXiv:1806.09896
  • Cabral-Marquesa O., Marquesa A., Giil L., De Vito R., Rademacherf J., Günther J., et al. Network-based analysis reveals signatures of IgG autoantibodies targeting G protein-coupled receptors in healthy and diseases

Conferences & Awards

Talks and Posters

  • Shared and Study-Specific Dietary Patterns, JSM 2018 (contributed talk and poster)
  • Bayesian Multi-Study Factor Analysis, 2018 ISBA World Meeting (invited talk)
  • Bayesian Multi-Study Factor Analysis in High-Dimensional Biological Data, JSM 2017 (invited talk)
  • Bayesian nonparametric multi-study factor analysis, BNP 11 Paris 2017 (contributed talk)
  • Shared and Study-Specific Dietary Patterns and their association with head and neck cancer risk in an international consortium, Mount Sinai, 2017 (seminar)
  • A novel approach to external reproducibility and validity of dietary patterns, 14th INHANCE Annual Meeting, 2017 (invited talk)
  • Multi-study factor analysis model, with applications in genomics and epidemiology, Universita’ degli studi di Milano, 2016 (invited talk)
  • Multi-study factor model in dietary pattern analysis, NIPS, 2016 (poster)
  • Indian Buffet process in factor analysis, WiML2016 (poster)
  • Bayesian Multi-study Factor Analysis, 2016 ISBA World Meeting (poster)
  • Multi-study Factor Analysis, Reproducibility in Personalized Medicine Research workshop, 2016 (poster)
  • Latent variables models for big data analysis, Princeton Neuroscience Institute Meeting, Princeton University, 2016 (invited talk)
  • Joint Factor Analysis in High Dimensional Biological Data, Computer Science, Princeton University, 2015 (invited talk)
  • Cross-study analysis for Biological Data, Genomic Meeting, Department of Biostatistic and Computational Biology Department, Dana Farber Cancer Institute, 2015 (contributed talk)
  • Joint analysis in High Dimensional Data, University of Padua, 2015 (seminar)


  • ISBA 2018 World Meeting Travel Support for the ISBA 2018 World Meeting in Edinburgh, June 2018
  • ISBA Travel Award for the BNP 11 Conference in Paris, June 2017
  • WiML Travel grant for the Women in Machine learning 2016 workshop, November 2016
  • Best Poster Award, Reproducibility in Personalized Medicine Research workshop, September 2016
  • ISBA Travel Award for ISBA World Meeting 2016 provided by Google, International Society for Bayesian Analysis, June 2016
  • Research Fellowship and Award for Study and Research Abroad, Department of Statistics, University of Padua, 2014 - 2015
  • MSc Thesis Award to conduct research at Harvard T.H. Chan School of Public Health, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 2011
  • Study Fellowship, Università di Roma - Sapienza, 2011 and 2010