His research focuses on developing and analyzing optimization-based methods to tackle inverse problems that arise in applications such as neuroscience, computer vision and medical imaging. Before joining NYU, he completed his PhD under the supervision of Emmanuel Candes at Stanford University and then spent a year at Google, where he worked on techniques to process neural data. IDeAS Seminar: A random walk on the upper triangular matrices