Research Projects

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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Spectrum: A Visual Analytics Tool to Explore Logs for VAST Challenge 2015

We present a visual analytics tool, called Spectrum, to analyze the movement and communication log data from VAST Challenge 2015. Spectrum has two views: MoveView and SpectrumView. MoveView gives an overview of the movement logs at a certain timestamp by synthesizing time, location and identity information. It replays movement logs over time and demonstrates communication logs with dynamic links. SpectrumView shows the status of all visitors' activities within a period of time. Each stay of visitors in a location is visualized as a line segment.

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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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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.

See the Andromeda demo video here: https://youtu.be/lyfUMCu-wC8

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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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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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