I am an interdisciplinary researcher interested in how attention is used to prioritise sensory input to meet an agent’s goals. Psychology and neuroscience have studied attention in humans for more than a century, but attention has recently become of interest to the machine learning community. My overarching research goal is to understand visual attention by developing machine learning models that simulate human visual processing. I use data from high-speed eye tracking and experiments that involve complex visual tasks like foraging, search and memorization of scenes to understand how attention is allocated and train computer models to do similar tasks. By understanding human attention better, we also learn new ways to think about and implement machine attention.