Carolyn Penstein Rosť
Associate Professor (Effective July, 2011)

A picture of me

Carnegie Mellon University
Language Technologies Institute
and HCI Institute
Gates-Hillman Center 5415
5000 Forbes Ave.
Pittsburgh, PA 15213-3891
+1 (412) 268-7130 (W)
+1 (412) 268-6298 (F)

Projects: Projects.html
Publications: Seclected Publications and Full Publication List
Teaching: Teaching.html
Full CV: 2012-CV-Rose-External.pdf
My Group: MyGroup.html
Recent Press Coverage: News.html
Outreach: Internship Program in Technology Supported Education
Personal: Stuff about Me
Goals After Tenure: Into the Future

Secretary/Treasurer of The International Society of the Learning Sciences
Associate Editor of the International Journal of Computer Supported Collaborative Learning

Statement of Career Goals

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.

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 in connection with outcomes that have real world value. The next step is to adapt, extend, and apply machine learning and text mining technologies in ways that leverage that deep understanding in order to build computational models that are capable of automatically applying these constructs to naturally occurring language interactions. Finally, with the technology to automatically monitor naturalistic language communication in place, the final stage is to build interventions that lead to real world benefits.

This approach leads to three aspects included in each project:

I am actively involved in efforts that have a clear path towards impacting individuals and communities around the world and transforming how they benefit from participation in computer-mediated interactions, including partnerships with The Math Forum, a major university based math service reaching millions of students each year, and Pittsburgh Public Schools, an urban K-12 public school district. In an effort to arrive at generalizable models, I am pursuing this research program in multiple parallel contexts that provide opportunities to investigate how both the manifestation of the conversational constructs as well as their effects on outcomes are nuanced through mediating contextual variables. I am conducting research in eight currently funded projects, many of which fall within my primary impact area of education:

  • Education at the middle school, high school, and college level
    • Investigating the Social and Communicative Factors in Learning (NSF)
    • Group Cognition: Learning in Engineering Project Teams (NSF)
    • Networked Collaboration Modules for Integrating Mathematics and Engineering Education Using Intelligent Agents (NSF)
    • Dynamic Support for Virtual Math Teams (NSF)
    • ENGAGE: Learning to solve problems, Solving problems to learn (DARPA)
  • Emergency response
    • Towards Optimization of Macrocognitive Processes: Automating Analysis of the Emergence of Leadership in Ad Hoc Teams (ONR)
  • Health and Wellbeing
    • Conversational Dynamics in Online Support Groups (NSF)
    • Extracting Social Meaning from Code Switching in English and French with Selected African Languages: Swahili, Zulu, Lingala, and Ciluba (ARL)