CMU Data Interaction Group

We are a research group at the Human-Computer Interaction Institute at Carnegie Mellon University. Our mission is to empower everyone to analyze and communicate data with interactive systems.

Learn more about our group

Research Areas

Our group conducts research in computer science at the intersection of human-computer interaction, machine learning, data science, programming languages, and data management.

Visualization

Visualization leverages human perception to make (potentially large) data accessible. We are developing new languages and tools for analysis and communication.

Human-Centered Data Science

While computers can help us manage data, human judgment and domain expertise is what turns it into understanding. Meeting the challenges of increasingly large and complex data requires methods that richly integrate the capabilities of both people and machines.

Interpretable Machine Learning

Machine learning allows data scientists to summarize, aggregate, and make predictions about their data. But computers don't explain their predictions, which limits interpretability and actionable insights. We create techniques and systems that make models and their decisions understandable.

Design for Machine Learning

To create ML-based applications, we need to understand and design for everyone involved in the development process—the engineers who build the models, the designers who create the experiences, and the people who use the products.

Lab Meetings

We have lab meetings on Mondays from 1-2 PM in our lab Newell-Simon Hall A408. If you are interested in attending, please email us.

Recent Publications

Show all
Vipera: Blending Visual and LLM-Driven Guidance for Systematic Auditing of Text-to-Image Generative AI Yanwei Huang, Wesley Hanwen Deng, Sijia Xiao, Motahhare Eslami, Jason Hong, Arpit Narechania, Adam Perer, CHI 2026
Show details
Toward Softerware: Enabling Personalization of Interactive Data Representations for Users With Disabilities Frank Elavsky, Marita Vindedal, Ted Gies, Patrick Carrington, Dominik Moritz, Øystein Moseng, IEEE Computer Graphics and Applications 2026
Show details
It's Not Just for Trust: Designing for Emerging Uses of Explainable AI in Clinical Decision-Making Katelyn Morrison, Zexuan Li, Minsuk Kim, Chengqi (Malia) Hong, Jidapa Kraisangka, Priscilla Correa-Jaque, Charles Fauvel, Sandeep Sahay, Rebecca R. Vanderpool, Allen Everett, Shili Lin, Manreet Kanwar, Raymond Benza, Adam Perer, ACM HEALTH 2026
Show details
Show all