BayesMendel Lab
The BayesMendel Lab develops sophisticated statistical models and software that identify individuals with elevated cancer risk due to inherited genetic factors, providing them with accurate risk assessments, and better access to detection and prevention strategies.
450 Brookline Ave
Boston, MA 02215
Our Team
Danielle Braun co-leads the BayesMendel Lab together with Giovanni Parmigiani. She is a Senior Research Scientist in the Biostatistics Department at Harvard T.H. Chan School of Public Health and in the Department of Data Science at Dana-Farber Cancer Institute. Her areas of research include risk prediction, genetic epidemiology, measurement error, survival analysis, frailty models, causal inference, and comparative effectiveness research.
Giovanni Parmigiani co-leads the BayesMendel Lab together with Danielle Braun. He is a professor of biostatistics in the Department of Biostatistics at Harvard T.H. Chan School of Public Health and in the Department of Data Sciences at Dana-Farber Cancer Institute.
Shirley Chen is a trainee in the BayesMendel Lab and a graduate student in computational biology and quantitative genetics at Harvard Chan School.
Nicolas Kubista is a trainee in the BayesMendel Lab and a research fellow in the Department of Biostatistics at Harvard Chan School.
Maria Sol Rosito is a trainee in the BayesMendel Lab and a research fellow in the Department of Biostatistics at Harvard Chan School and the Department of Data Science at Dana-Farber Cancer Institute.
Jintong Zhao is a biostatistician in the Department of Biostatistics at Harvard Chan School.