Joan Bruna graduated from Universitat Politecnica de Catalunya (Barcelona, Spain) in both Mathematics and Electrical Engineering. He obtained an M.Sc. in applied mathematics from ENS Cachan (France). He then became a research engineer in an image processing startup, developing real-time video processing algorithms. He obtained his PhD in Applied Mathematics at Ecole Polytechnique (France), under the supervision of Prof. Stephane Mallat. He was a postdoctoral researcher at the Courant Institute, NYU, New York, and a postdoctoral fellow at Facebook AI Research. In 2015, he became Assistant Professor at UC Berkeley, Statistics Department, and starting Fall 2016 he joined the Courant Institute (NYU, New York) as Assistant Professor in Computer Science, Data Science and Mathematics (affiliated). His research interests include invariant signal representations, high-dimensional statistics and stochastic processes, deep learning and its applications to signal processing
My work centers on the task of building data-driven representations to solve complex problems arising in several areas of computer vision, physical sciences and statistics. In particular, I am developing neural network models that operate on general data domains such as graphs, with specific applications to particle physics anomaly detection.