Research Projects

Immersive Analytics

Immersive Space to Think (IST) builds on our prior work on "space to think" and "semantic interaction" with large 2D displays for sensemaking. IST is a novel type of immersive analytics that leverages two key aspects of immersive virtual reality (VR) and augmented reality (AR) technologies to support analytic synthesis. First, immersive technologies can provide nearly unlimited 3D space that allows analysts to use space in more expressive and nuanced ways to organize data.

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Andromeda: Semantic Interaction for Dimension Reduction

Andromeda enables users to directly manipulate the data points in 2D plots of high-dimensional data to explore alternative dimension reduction projections. Andromeda implements interactive weighted multidimensional scaling (WMDS) with semantic interaction. Andromeda allows for both parametric and observation-level interaction to provide in-depth data exploration. A machine learning approach enables WMDS to learn from user manipulated projections.

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Smooth Interactive Visualization

This research examines the use of signal processing theory to improve the smoothness of interactive visualization, producing visualizations that are more beautiful and more rapidly interactive, even for big data.

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GPU Based Methods for Interactive Information Visualization of Big Data

Interactive visual analysis has been a key component of gaining insights in information visualization area. However, the amount of data has increased exponentially in the past few years. Existing information visualization techniques lack scalability to deal with big data, such as graphs with millions of nodes, or millions of multidimensional data records.

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CHITA Project

Along with members of the BaVA Group and statistics students, we are working with General Dynamics to create a new data analytics system.

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Visual Analytics with Biclusters

Identifying coordinated relationships is an important task in data analytics. For example, an intelligence analyst might want to find three suspicious people who visited the same four cities. However, existing techniques that display individual relationships, such as between lists of entities, require repetitious manual selection and significant mental aggregation in cluttered visualizations to find coordinated relationships.

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Be the Data: Embodied Visual Analytics

"Be the Data" is a physical and immersive approach to visual analytics designed for teaching abstract statistical analysis concepts to students. In particular, it addresses the problem of exploring alternative projections of high-dimensional data points using interactive dimension reduction techniques. In our system, each student literally embodies a data point in a dataset that is visualized in the room of students; coordinates in the room are coordinates in a two-dimensional plane to which the high-dimensional data are projected.

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Semantic Interaction Project

The goal of this project is to enable the creation of new human-centered computing tools that will help people effectively analyze large collections of textual documents by providing powerful statistical analysis functionality in a usable and intuitive form. To accomplish that, this project investigates “semantic interaction” in visual analytics as a method to combine the large-data computationally-intensive foraging abilities of formal statistical mining algorithms with the intuitive cognitively-intensive sensemaking abilities of human analysts.

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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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