His research interests touch several areas of Machine Learning, Signal Processing and High-Dimensional Statistics. In particular, in the past few years he has been working on Convolutional Neural Networks, studying some of its theoretical properties, extensions to more general geometries, and applications to physical sciences and statistics. Before that, he worked at FAIR (Facebook AI Research) in New York. Prior to that, he was a postdoctoral researcher at Courant Institute, NYU. He completed his PhD in 2013 at Ecole Polytechnique, France. He is the recipient of an Alfred. P. Sloan Fellowship (2018), and he has organized multiple tutorials and workshops on geometric deep learning, including NIPS and CVPR in 2017.