My research aims to advance the state-of-the-art in data visualization. While a large effort of mine has been to exploit high performance computing for achieving interactive visualization or just making possible large data visualization, I have also begun to look into the exploratory and user interface aspects of the data visualization problem. An important concept I have introduced is that the user interface of a visualization system is the visualization displayed. One key approach is to integrate machine intelligence into the process of visualization. Scientists are then able to concentrate on data exploration and interpretation rather than on user interface artifacts. It's also very beneficial to be able to reuse and share scientists' visualization experience. The new visualization technologies my research team is developing can therefore drastically raise scientists' visualization productivity by allowing them to verify their understanding and more effectively communicate and share with others their findings. I am very glad to see many others have joined me in the same research endeavor.

Specific research directions of interest include:

		Information Visualization Our research in information visualization focuses on very large graph visualization, visual data mining, social network analysis, and computer security visualization. We are also interested in studying the information visualization aspect of scientific visualization problems. [More Information]
		Parallel & Distributed Visualization 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. [More Information]
		User Interfaces 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. [More Information]
		Expressive Visualization 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. [More Information]
		Volume Visualization 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. [More Information]
		Time-Varying Volume Data 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. [More Information]

Current research projects are sponsored by NSF PECASE, NSF LSSDSV, NSF ITR, DOE ASCI, DOE SciDAC, and NIH Human Brain Project.