His main research interests are in theoretical and computational geometric models and their applications to problems from the fields of computer vision, graphics, and machine learning. At Radcliffe, Bronstein will be working on developing formulations of deep learning for non-Euclidean structured data such as graphs and manifolds, which are becoming increasingly important in a variety of fields including computer vision, sensor networks, biomedicine, genomics, and computational social sciences. He hopes that new geometric deep learning paradigms will help achieve quantitatively and qualitatively better results in these fields.