I am a Partner Researcher and manager of the Augmented Learning and Reasoning group in Microsoft Research. I am interested in the development, improvement, and analysis of machine learning methods with a focus on systems that can automatically understand natural language, learn from multimodal interactions, and augment the capabilities of people.
My past work has examined a variety of areas — primarily personalization, contextually intelligent assistants, ensemble methods, calibrating classifiers, large-scale classification, search query classification and characterization, and redundancy and diversity, but also extending to transfer learning, machine translation, recommender systems, knowledge bases, and human computation and preferences. In addition to my research, I engage in a variety of professional service activities for the machine learning, data mining, and information retrieval communities.
Before coming to Microsoft, I obtained my Ph.D. from the Computer Science Department at Carnegie Mellon University under Jaime Carbonell and John Lafferty. Prior to that I worked with Ray Mooney and Robert Causey during my undergraduate days in the Computer Science, Philosophy, and Plan II Honors departments at the University of Texas at Austin.
Building contextually intelligent assistants with Dr. Paul Bennett
Episode 59, January 16, 2019 - Dr. Bennett brings us up to speed on the science of contextually intelligent assistants, explains how what we think our machines can do actually shapes what we expect them to do, and shares how current research in machine learning and data science is helping machines reason on our behalf in the quest to help us find the right information effortlessly.