Faculty develop methods for structured and unstructured biomedical data that advance statistical inference, machine learning, causal inference, and algorithmic modeling. Their work delivers principled ...

Recent technological advances have enabled the production of vast amounts of data types that can help health researchers better understand complex diseases, such as cancer, cardiovascular diseases and ...

High-dimensional -omics data such as genomic, transcriptomic, and metabolomic data offer great promise in advancing precision medicine. In particular, such data have enabled the investigation of ...

Our laboratory has developed a range of data analysis workflows that incorporate advanced statistical and computational methods to interpret the complex molecular datasets generated by MS technologies ...

Faculty in the Statistics in Epidemiology Hub develop statistical methods to guide population-level research on cancer prevention, early detection, and real-world outcomes. Their work supports the ...

Cambridge, MA – Let’s say an environmental scientist is studying whether exposure to air pollution is associated with lower birth weights in a particular county. They might train a machine-learning ...