I'm broadly interested in machine learning and computer vision, and am particularly interested in learning compact but semantically rich representations of our world. To me, this can mean finding better/cheaper/faster ways of training representations which generalize well to new distributions. It can also mean efficient adaptation to target data. Finally, it can mean finding ways to skip the pre-training step and efficently train on the target data.