I am interested in general machine learning and computer vision problems. During PhD, I was interested in robust geometric perception, which estimates computer vision models (correspondences between images, poses, 3D reconstructions) given outlier contaminated data. I was specifically interested in designing efficient algorithms that have optimality guarantees, i.e., guarantee to return the best solution. After joining Intel, my interests shift towards a mixture of learning and vision, where I study various problems such as 1) learning-based perception (feature matching, finding correspondences, pose estimation, depth estimation etc) 2) Continual Learning 3) Generative models (e.g., novel view synthesis, image/3D scene generation). My work has been selected as one of the 12 best papers at ECCV'18.