My main research interest is computer vision, i.e. the intersection of computer science, machine learning and artificial intelligence that investigates methods of analyzing and understanding the visual world in the form of images and videos. I am advised by David Crandall.
Originating from a collaboration with Chen Yu and his Computational Cognition and Learning Laboratory comes a specialization in first-person (egocentric) video that approximates a person's view. I am interested in why, how and when we look at things (and also how infants learn to do so) and how computer vision algorithms could predict or emulate human behavior. To that end, I have developed robust methods to locate and distinguish hands during complex interactions between the observer and the world. More generally, I am interested in a broad range of vision applications and have worked on tracking, recognition, detection, and scene classification problems using state-of-the-art techniques such as deep learning with convolutional neural networks.
