most citedOn Regulatory and Organizational Constraints in Visualization Design and Evaluation

11 citations · 16 across the 5 of their papers we have counts for

collaborators

5 papers

cs.HC20235 cited

Conversational AI Threads for Visualizing Multidimensional Datasets

Matt-Heun Hong, Anamaria Crisan

Generative Large Language Models (LLMs) show potential in data analysis, yet their full capabilities remain uncharted. Our work explores the capabilities of LLMs for creating and r…

cs.HC2023

RekomGNN: Visualizing, Contextualizing and Evaluating Graph Neural Networks Recommendations

Camelia D. Brumar, Gabriel Appleby, Jen Rogers +4

Content recommendation tasks increasingly use Graph Neural Networks, but it remains challenging for machine learning experts to assess the quality of their outputs. Visualization s…

cs.HC2023

Eliciting Model Steering Interactions from Users via Data and Visual Design Probes

Anamaria Crisan, Maddie Shang, Eric Brochu

Domain experts increasingly use automated data science tools to incorporate machine learning (ML) models in their work but struggle to "debug" these models when they are incorrect.…

cs.HC2023

Tracing and Visualizing Human-ML/AI Collaborative Processes through Artifacts of Data Work

Jennifer Rogers and, Anamaria Crisan

Automated Machine Learning (AutoML) technology can lower barriers in data work yet still requires human intervention to be functional. However, the complex and collaborative proces…

cs.HC201611 cited

On Regulatory and Organizational Constraints in Visualization Design and Evaluation

Anamaria Crisan, Jennifer L. Gardy, Tamara Munzner

Problem-based visualization research provides explicit guidance toward identifying and designing for the needs of users, but absent is more concrete guidance toward factors externa…