Dialogue-based tutoring at scale: Design and challenges
- RIPI-ICLS 2018
Publications: Maria's Google Scholar Profile
Maria Chang is a Research Staff Member at IBM Research AI. Her long-term goal is to build hybrid neuro-symbolic systems that can acquire a broad spectrum of knowledge and use that knowledge in humanlike ways. She leads efforts to understand complex events described in natural language by reasoning over symbolic knowledge bases and performing inference over learned representations. She has also made key technical contributions to intelligent tutoring systems that use mixed initiative dialogue and sketch understanding. Her work has appeared in a variety of AI and cognitive science conferences and journals, including AI magazine, AAAI, IAAI, Topics in Cognitive Science, Spatial Cognition, and Neuroimage. She received a PhD in computer science from Northwestern University (2016) and a BA in cognitive science from UC Berkeley.