Lab events at VIS 2014

VAST TVCG Paper: A Five-Level Design Framework for Bicluster Visualizations
Authors: Maoyuan Sun, Chris North, Naren Ramakrishnan

VAST Conference Paper: Multi-Model Semantic Interaction for Text Analytics
Authors: Lauren Bradel, Chris North, Leanna House, Scotland Leman

VAST Challenge Poster: Event-based Visual Analytics for VAST Challenge 2014
Authors: Ji Wang, Lauren Bradel, Peng Mi, Chris North

VIS 2014 MeetUp: Visual Analytics in the Classroom
Organizer: L. House, S. Leman, C. North, L. Bradel

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Event-Based Text Visual Analytics

Wang J, Bradel L, North C. Event-Based Text Visual Analytics. In: VAST Challenge 2014. VAST Challenge 2014. Paris, France; 2014. p. .

Event-based Visual Analytics for VAST Challenge 2014

We present an event-based approach for solving a directed sensemaking task in which we combine powerful information foraging tools with intuitive synthesis spaces to solve the VAST 2014 Mini-Challenge 1 (MC1). A combination of student-created and commericially available software are used to solve various aspects of the scenario.


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Team awarded NSF Big Data grant

BIGDATA: F: DKA: Usable Big Data Analytics via Multi-Scale Visual to Parametric Interaction (MV2PI)

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Alex Endert joins the Faculty of Georgia Tech

Laboratory alumnus Dr. Alex Endert accepted a tenure track faculty position as an Assistant Professor in the School of Interactive Computing at Georgia Tech beginning this Fall 2014. He began the Fall semester teaching a graduate course on Information Visualization.

See Alex's GaTech page here

Congratulations Alex!

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StarSpire: Multi-Model Semantic Interaction for Text Analytics

Semantic interaction offers an intuitive communication mechanism between human users and complex statistical models. By shielding the users from manipulating model parameters, they focus instead on directly manipulating the spatialization, thus remaining in their cognitive zone. We present the concept of multi-model semantic interaction, where semantic interactions can be used to steer multiple models at multiple levels of data scale, enabling users to tackle big data problems. We also present an updated visualization pipeline model for generalized multi-model semantic interaction.

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