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Expert Inquiry

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Examining computational thinking processes in modeling unstructured data

Nine elements for robust collaborative learning analytics: A constructive collaborative critique

High school students’ data modeling practices and processes: From modeling unstructured data to evaluating automated decisions

Examining socially shared regulation and shared physiological arousal events with multimodal learning analytics

Adapting to the Long Tail: A Meta-Analysis of Transfer Learning Research for Language Understanding Tasks

Carolyn Penstein Rosé's research looks to understand human conversation and use this understanding to build computer systems that can improve the efficacy of conversation between people, and between people and computers. In pursuit of these goals, she utilizes approaches from computational discourse analysis and text mining, conversational agents and computer supported collaborative learning. Her work is grounded in the fields of language technologies and human-computer interaction.

Language Technologies

Human Conversation

Text Mining

Human-Computer Interaction

Computational Discourse Analysis

Conversational Agents

Computer Supported Collaborative Learning

Carnegie Mellon University Profile 

Google Scholar Citations

Wikipedia Page

The AI company Elon Musk cofounded just released a 'groundbreaking' tool that can automatically mimic human writing — here's how stunned developers are experimenting with it so far

Elon Musk-Backed AI Company Launches New Tool that Writes Naturally Like Humans

Online Classes Get a Missing Piece: Teamwork

How Conversation Impacts Learning

Professor

Invited Talk 1: Carolyn Penstein Rosé

e-Literate TV interview with Carolyn Rose from Carnegie Mellon University about her research into how to match students up with each other to increase discussion and understanding in MOOCs. Recorded at the MOOC Research Initiative conference in December 2013. #MRI13

MRI13 Interview of Carolyn Rose

Join CMU professors Carolyn Rose, Zachary Chase Lipton and Eric Nyberg, along with government and industry professionals Danica Marinac-Dabic and Sandeep Konam, for a panel discussion covering current research trends in the understanding and analysis of medical conversations. Topics include extracting information from medical conversations and creating usable summaries of medical provider/patient conversations.
Recent work on dialogue modeling, medical entity extraction, and event and ordering extraction are discussed, as well as the completion of knowledge graphs. CMU scientists are working with the National Institutes of Health, healthcare companies and large technology providers to better understand key challenges relating to this work.

Panelists:

Carolyn Penstein Rosé
Professor, Language Technologies Institute and Human-Computer Interaction Institute, School of Computer Science

Zachary Chase Lipton
Assistant Professor, Operations Research, Tepper School of Business and Machine Learning Department, School of Computer Science

Sandeep Konam
Co-Founder/CTO - Abridge

Danica Marinac-Dabic
Associate Director, Office of Clinical Evidence and Analysis Center for Devices and Radiological Health
U.S. Food and Drug Administration

Moderator:

Eric Nyberg
Professor, Language Technologies Institute, School of Computer Science

Medical Dialogue Virtual Roundtable

Carolyn Penstein Rosé

Media Inquiry