I am interested in the computational basis of human perception, decision making, and learning. My focus has been on two related questions in visual systems neuroscience and machine learning: understanding how different patterns of neural activity give rise to specific human object recognition behaviors; and developing high-performing computer vision models that in turn predict this neural activity from input images. Answers to these two questions provide an understanding of object recognition in the human visual system as a full pipeline from images to behavior. By understanding vision in the brain better, I believe we will be able to discover more effective computer vision algorithms, and conversely, such improved algorithms will allow us to gain insight into how the brain works.