About Me [ PDF ]

My goal is to boost human learning and performance by developing and applying formal models of cognition. I am interested in producing software that enables individuals to learn and perform tasks efficiently and effortlessly. My approach draws on methods from machine learning and theories from cognitive science in order to construct robust psychological models that characterize the computational challenges faced by an individual attempting to complete a task. My research lies at the interface of human learning, machine learning, and computer-assisted decision making.

My research has predominantly focused on helping individuals categorize visual images. I have approached this objective from two perspectives: decision support and efficient training. Decision support enables expert-like levels of performance—without training—by exploiting ordinary but powerful human visual capabilities. Efficient training promotes the discovery of the visual features necessary to correctly categorize the images. Both approaches leverage a latent space representation of human-perceived similarity, which we refer to as a psychological embedding.

Current limitations in visual task performance motivate my primary research questions:

• What kinds of visual tasks would benefit most from a computer-assisted training paradigm?
• What types of training will make acquiring visual expertise more efficient?
• What types of decisional support will enable relatively effortless but accurate performance on visual tasks?