State of the art learning-based systems require enormous, costly datasets on which to train supervised models. To progress beyond this requirement, we need learning systems that can interact with their environments, collect feedback, and improve over time. In most real-world settings, such feedback is sparse and delayed.
Amr Sharaf is a Ph.D. student in the Computational Linguistics and Information Processing (CLIP) Lab of the Department of Computer Science at the University of Maryland, advised by Hal Daumé III. His research focuses on developing interactive learning algorithms in the context of structured prediction for artificial intelligence and natural language processing.
Amr Sharaf is a Ph.D. student working with Hal Daume III.