Life Sciences Analytics

The ever-growing amount of available data, as issued from biological and/or genetic measurements or as features from medical images, allow life sciences researchers to breach the frontiers of knowledge in many directions, outside the controlled experimental settings. Data analytics, in this context, consists in using and developing statistical methods that can control population (or out-of-sample) validity (e.g. under sampling bias, measurement error, etc.), by controlling the decisional risk associated to hypothesis testing and/or prediction. We contribute to the application and development of new data analytics methods, in high dimensions, for estimation, prediction and/or model selection.