Developing adaptive learning algorithms that are data-efficient and scalable
About
Julian Katz-Samuels is from New York state. He is an avid and relentlessly optimistic New York Knicks fan. He enjoys playing basketball, playing poker, and discussing politics.
Education
- PhD, Electrical and Computer Engineering, University of Michigan
- BA, Mathematics and Philosophy, University of Chicago
Research Description
Julian Katz-Samuels' research focuses on designing algorithms for interactive learning, where the algorithm collects data and makes decisions based on previously observed feedback. He has worked in areas ranging from multi-armed bandits and linear bandits to active classification.