Data is everywhere, but without a means to understand it, data is fundamentally useless. One avenue to make data meaningful is the use of visualization—interactive computer graphics for visually analyzing information. My expertise is in both spatial/scientific visualization (visualization of 3D structures, such as volume visualization volume visualization [Volume Graphics 2001]) and non-spatial/information visualization (visualization of abstract data, such as social networks [InfoVis 2003]). I work with data stakeholders on their tasks while at the same time delving the limits of visualization. Toward these ends, I focus on visualization theory, interactive visual analytics, and applications such as bioinformatics and security visualization. Fundamentally, I help others solve their problems.

I have had some success in funding and have several publications covering my research. This page provides an overview of my work; further information may be found on my CV.

Research areas

Visualization Theory Visualization and visual analytics are part engineering (to solve problems) and part science (to suggests which solutions are best). I have spent the last decade investigating the science of visualization [Springer 2009]—-developing models and frameworks for improving visualization design. The first facet of this research involves improving and understanding interaction. For example, we developed a spreadsheet-like interface for exploring the space of visualization parameters (the factors which control what is seen) [TVCG 2001]; the spreadsheet acts as a window into the higher-dimensional parameter space with intuitive metaphors for navigation. The spreadsheet formalism also allows the sharing of visualization results on the desktop and on-line [IEEE Computer Graphics & Applications 2003]. This formalism was built upon observations of how users explore via visualization interfaces, leading to theories on how to improve the flow of visual exploration [Information Visualization 2011].

Using our understanding of visualization interfaces as a basis, I have built a body of work encapsulating the essence of visual exploration. Efforts in this area include a formal model of the visual exploration process [IEEE TVCG 2007] which characterizes the fundamental operation of visual exploration and provides a formal method for reasoning about such explorations [IPAR 2008]; essentially, every visualization generated consists of parameters, the method of generation, the generated result, and provenance that captures their interrelationships. These relationships can themselves be visualized, and we have proposed metrics to measure the efficacy of visualization interfaces based upon factors such as efficiency of exploration and depth of the parameter space investigated. This work led to award winning panels at IEEE Visualization 2006 and VisWeek 2011, and I have collaborated with others to foster workshops to further develop this important research area.

Visual Analytics Visualization integrated with interactive statistics is a powerful tool for making data actionable. In the last 5 years, we have worked with police officers and hurricane scientists to assist them with such analytic tools. For forensics investigators, we adapted visualization methods for text forensics [Information Visualization 2011]. Starting with an NSF-funded empirical study of the officers, we found inefficiencies that could be assisted by visualization. The data for the visualization is generated by our hard-disk analytics system; this system converts gigabytes of files into a database for finding connections in the textual data. Our second visual analytics system uses an interactive coupling of visualization and analytics to find correlations in hurricane trends [Computers and Geosciences 2009]; in contrast to the the forensics system, the hurricane analytics are initiated dynamically by the visualization which in-turn depicts the newly generated results. This close coupling facilitates hypothesis-exploration of the complex meteorological data. In both cases, the visual and the computational benefited our collaborators.

Interdisciplinary Visual Data Science In addition to the aforementioned work, I apply visualization to novel domains that integrate both scientific and information visualization. I contribute regularly to computer security visualization; efforts include systems for routing anomaly analysis [IEEE CG&A 2004], cluster traffic monitoring [ACMSE 2007], and the aforementioned forensics work. My long term collaboration with biologists resulted in several systems (e.g., effective multiple-domain visualization [BMC Bioinformatics 2009] and depictions of functional genomics experiments [BMC Bioinformatics 2010]). I have also contributed to liquid crystal physics by visualizating nematic liquid crystal behavior [IEEE TVCG 2006]) with provably superior communication of the physical behavior [Computer Graphics Forum 2010]. Visualization is interdisciplinary by nature, and I am constantly looking for additionally collaborators whom I can assist.

Student Research

I am always interested in talking to students at State about joining my research group; please see my student information page for information about my current projects.