Data Visualization
Visualization leverages human perception to analyze potentially large amounts of data and make it easier for us to understand.
Using graphical or visual formats to represent data, such as charts, graphs, maps and dashboards, enables us to make complex data more accessible. It can help in identifying patterns, trends, and insights that might not be immediately apparent from raw data. Relatedly, computational data science techniques contribute to data visualization as well. Machine learning models and their applications allow data scientists to analyze large amounts of complex data, but our focus on human-centered data science pairs the people with domain expertise along with the computer’s capabilities.
The CMU Data Interaction Group develops new languages and tools for analysis and communication.
Students who want to learn more about this HCI research area might be interested in the following courses:
CMU at CHI 2025
NEWSOur halls are abuzz with talk of “CHI” (pronounced “kai”), the nickname for the upcoming conference on computer human interaction (CHI)....
Predicting and Visualizing Overdose Risk for Public Health
PROJECTOverdose due to opioid misuse and abuse is currently a critical public health issue in the United States and worldwide. Machine learning (ML) approaches h...
Data Navigator
PROJECTData Navigator is a JavaScript library that allows for navigation of data structures. Data Navigator makes png, svg, canvas, and even webgl graphics acces...
Chartability
PROJECTChartability is a set of heuristics (testable questions) for ensuring that data visualizations, systems, and interfaces are accessible. Chartability is or...
CMU Researchers Release Zeno for Machine Learning Model Evaluation
NEWSA team of Carnegie Mellon University researchers has released a new interactive platform for data management and machine learning (ML) evaluation called Zeno....