Carolyn Rosé

My research program is focused on better understanding the social and pragmatic nature of conversation, and using this understanding to build computational systems that can improve the efficacy of conversation between people, and between people and computers. In order to pursue these goals, I invoke approaches from computational discourse analysis and text mining, conversational agents, and computer supported collaborative learning. I ground my research in the fields of language technologies and human-computer interaction, and I am fortunate to work closely with students and post-docs from the Language Technologies Institute and the Human-Computer Interaction Institute, as well as to direct a lab of my own, called TELEDIA. My group’s highly interdisciplinary work, published in 150 peer reviewed publications, is represented in the top venues in 5 fields: namely, Language Technologies, Learning Sciences, Cognitive Science, Educational Technology, and Human-Computer Interaction, with awards or award nominations in 3 of these fields.

My research towards this end has birthed and substantially contributed to the growth of two thriving inter-related areas of research: namely, Automated Analysis of Collaborative Learning Processes and Dynamic Support for Collaborative Learning, where intelligent conversational agents are used to support collaborative learning in a context sensitive way. The key idea behind all of my work is to draw insights from rich theoretical models from sociolinguistics and discourse analysis, and pair them down to precise operationalizations that capture the most important essence of what is happening for achieving impact. My approach is always to start with investigating how conversation works and formalizing this understanding in models that are precise enough to be reproducible and that demonstrate explanatory power i