Danielle Braun is a Principal Research scientist in the Biostatistics Department at the Harvard T.H. Chan School of Public Health and at 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, clinical tool development, causal inference, and environmental health. As a Principal Research Scientist Danielle co-leads the BayesMendel lab and is Director of Data Science for Environmental and Climate Health in the NSAPH group.

Software Contributions

BayesMendel R package: BayesMendel
PanelPRO R package: Fam3PRO
ASK2ME Risk Prediction Tool: ASK2ME
MyLynch: MyLynch
CausalGPS R package: CausalGPS

My name is Daniel Mork and I develop statistical machine learning tools for precision environmental health. My work has been applied to understand individual risk factors and identify harmful chemicals for how repeated exposures impact perinatal health, and to study the long-term effects of air pollution in risks for neurological disease including Alzheimer’s and Parkinson’s diseases. Additionally, I specialize in methods for causal inference to quantify the effects of exposures within large-scale observational datasets, including Medicare. My work is backed up by comprehensive statistical and data science tools for reproducible research.