In every sector of human activity, the pervasiveness of sensors and the accumulation of digital information have raised novel intellectual challenges, dreams and fears. Recently, intensive research in the field of high dimensional statistics, the progress in the description and modeling of networks, and the second life of optimization theory have generated concepts and algorithms that allow to develop inference on complex data and also to think about new perspectives of interactions between experts or scientists of different fields. A major tension when addressing such issues from the viewpoint of applications is the balance between customization and reproducibility and, to my opinion, these two criteria should drive future innovations in the field of machine learning. In the talk, I will illustrate these ideas by going through a few recent achievements arising from interdisciplinary projects in the fields of digital marketing, fluid mechanics, and ethomics.
Nicolas Vayatis (ENS Cachan)