My research aims to advance the state-of-the-art in data visualization.
While my early work was largely driven by scientific applications, since I joined UC Davis
I have expanded the scope of my research to address challenges presented by a wide variety
of real-world applications. I and my students have developed visualization solutions
for assisting in making business strategies based on customer data, fighting crime,
detecting and tracking intrusions for network security, understanding large-scale scientific
research data, analysis of biomedical image data for better medical diagnosis and surgical planning,
supercomputing resource management and performance optimization, and many others.
My group pioneers several new design concepts and
technical approaches for solving complex visualization problems.
One such concept is visualization is the interface.
Visualization is designed to let the user interact with the
visualization directly and concentrate on data exploration
and interpretation tasks rather than on user interface artifacts.
The interface should also enable reuse and sharing of collective
visualization experience among users.
We pioneer incorporating machine intelligence into the process of
interactive volume data classification and visualization.
We show how to use visualization to understand machine learing.
We also demonstrate convincingly the feasibility and
value of in situ visualization for very large-scale scientific
simulations, and we introduce the concept of explorable images
as an alternative solution for very large-scale 3D field data visualization.
The new visualization technologies my group introduces
can therefore drastically raise scientists' productivity
by allowing them to verify their understanding
and more effectively communicate and share with others their findings using
visual means. We are presently pursuing a series of usability studies
to further enhance the value of visualization technology in practical settings.
We are also studying how to better support scientists to do storytelling from
their research data. I continue to launch new threads of research in data
visualization and for developing new visualization applications.
See our research highlights.
Specific research directions of interest include:
We are entering a data-rich era. Advanced computing, imaging, and sensing technologies enable scientists to study natural and physical phenomena at unprecedented precision, resulting in an explosive growth of data. The size of the collected information about the Web and mobile device users is expected to be even greater. To make sense and maximize utilization of such vast amounts of data for knowledge discovery and decision making, we need a new set of tools beyond conventional data mining and statistical analysis. Visualization has been shown very effective in understanding large, complex data, and thus become an indispensable tool for many areas of research and practice. We have been developing new concepts to further advance the visualization technology as a powerful discovery and communication tool.
The ability to do data triage and visualization during a simulation run becomes more and more essential as scientific supercomputing moves from terascale and petascale to exascle. We have been studying the feasibility and requirements of in situ processing over the past few years. In situ analysis and visualization is clearly on our national research roadmap, and many others are joining us in this very important direction.
Our research in information visualization focuses on very large graph visualization, visual data mining, social network analysis, software evolution, and computer security visualization. We are also interested in studying the information visualization aspect of scientific visualization problems.
We aim to add to existing visualization systems the support for storytelling using illustrative visualizations, animations, annotations, and sound.
Visualizing large, complex volume data demands parallel or Grid-based solutions. We intend to realize a fully parallelized visualization pipeline. We have developed scalable parallel rendering algorithms for a variety of volume data, designed highly efficient software and hardware image compositing solutions, and built clusters targeting large-scale visualization applications. Most of the performance studies have been done on the massively parallel computers operated at LANL, LBL, LLNL, and PSC.
We develop visual interfaces that can help scientists keep track of their visualization experience and findings, use it to generate new visualizations, and share it with others. We also investigate how intelligent systems can assist sophisticated, time-consuming visualization tasks, and consequently simplify the user interfaces for performing the tasks.
We aim at improving the expressiveness of visualizations through the use of artistic inspired methods, non-photorealistic rendering techniques, and highly interactive user interfaces. Visualizations should be made by using the appropriate level of abstraction according to the purpose of visualization, and the visualizations should be perceptually effective to deliver the most relevant information in the data.
Volume data arises in a large subset of scientific, engineering, and biomedical applications. We aim to develop new methodologies for more efficient and effective volume segmentation and visualization.
Time-varying data visualization presents some unique challenges. Our goal is to drastically improve the interactivity and explorability of large-scale, time-varying data visualization through the study and development of innovative data reduction methods, rendering and interaction techniques, and system integration strategies tailored to the characteristics of several representative leading-edge applications. Check out our new NSF ITR project.
Current research projects are sponsored by HP Labs, Nokia Research, AT&T Labs, NSF FODAVA, NSF PetaApps, NSF HECURA, NSF CyberTrust, DOE SciDAC, DOE BER, and Air Force STTR.