Professor Learned-Miller’s interests can be broadly categorized as applying ideas and methods from machine learning to problems in machine vision. Problems he has worked on include learning from a small number of examples, independent component analysis, learned color constancy, developing probability models of shape deformation, and mathematical expression recognition. His Ph.D. thesis focuses on using learned statistical knowledge from one visual task to speed learning of a new, related task. The two major types of statistical knowledge used are distributions over shape variability and distributions over joint color variability. He describes a handwritten digit classifier that gets about 90% accuracy using only a single example of each handwritten digit.