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Alex Endert

Researcher at Georgia Institute of Technology

Publications -  127
Citations -  4227

Alex Endert is an academic researcher from Georgia Institute of Technology. The author has contributed to research in topics: Visual analytics & Visualization. The author has an hindex of 30, co-authored 122 publications receiving 3261 citations. Previous affiliations of Alex Endert include Smith College & University of Virginia.

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Proceedings ArticleDOI

Space to think: large high-resolution displays for sensemaking

TL;DR: Examining how increased space affects the way displays are regarded and used within the context of the cognitively demanding task of sensemaking finds both external memory and a semantic layer are affected.
Proceedings ArticleDOI

Semantic interaction for visual text analytics

TL;DR: In this article, the authors propose a new design space for visual analytic interaction, called semantic interaction, which seeks to enable analysts to spatially interact with models directly within the visual metaphor using interactions that derive from their analytic process, such as searching, highlighting, annotating, and repositioning documents.
Journal ArticleDOI

Characterizing Provenance in Visualization and Data Analysis: An Organizational Framework of Provenance Types and Purposes

TL;DR: This organization is intended to serve as a framework to help researchers specify types of provenance and coordinate design knowledge across projects and can be used to guide the selection of evaluation methodology and the comparison of study outcomes in provenance research.
Journal ArticleDOI

Information visualization on large, high-resolution displays: issues, challenges, and opportunities

TL;DR: Critical design issues are presented and some of the challenges and future opportunities for designing visualizations for large, high-resolution displays are outlined and it is hoped that these issues, challenges, and opportunities will provide guidance for future research in this area.
Journal ArticleDOI

The State of the Art in Integrating Machine Learning into Visual Analytics

TL;DR: This state‐of‐the‐art report presents a summary of the progress that has been made by highlighting and synthesizing select research advances and presents opportunities and challenges to enhance the synergy between machine learning and visual analytics for impactful future research directions.