2. A Visual Analytics Approach to Exploring Protein Flexibility Subspaces, Scott Barlowe, Jing Yang, Donald J. Jacobs, Dennis R. Livesay, Jamal Alsakran, Ye Zhao, Deeptak Verma, James Mottonen, Proceedings of IEEE Pacific Visualization, March, 2013, IEEE (LNK)(Bibtex).

Abstract: Understanding what causes proteins to change shape and how the resulting shape influences function will expedite the design of more narrowly focused drugs and therapies. Shape alterations are often the result of flexibility changes in a set of localized neighborhoods that may or may not act in concert. Computational models have been developed to predict flexibility changes under varying empirical parameters. In this paper, we tackle a significant challenge facing scientists when analyzing outputs of a computational model, namely how to identify, examine, compare, and group interesting neighborhoods of proteins under different parameter sets. This is a difficult task since comparisons over protein subunits that comprise diverse neighborhoods are often too complex to characterize with a simple metric and too numerous to analyze manually. Here, we present a series of novel visual analytics approaches toward addressing this task. User scenarios illustrate the utility of these approaches and feedback from domain experts confirms their effectiveness.

3. Biomedical volume rendering with Prof. Arie Kaufman and Prof. Klaus Mueller, Stony Brook University

The Magic Volume Lens: An Interactive Focus+Context Technique for Volume Rendering. Lujing Wang, Ye Zhao, Klaus Mueller and Arie Kaufman, In Proceedings of IEEE Visualization 2005,  pages 367-374 (PDF)(Bibtex).

Abstract: Massive taxi trajectory data is exploited for knowledge discovery in transportation and urban planning. Existing visual analytics methods typically retrieve trajectories by selecting geospatial points or regions on a map. The data queries are facilitated by carefully designed grid or tree structures. In many real-world tasks, domain and public users want to conveniently search and analyze taxi trajectories by combined conditions based on semantic information such as street and POI names, which however, cannot be easily supported by existing methods. To fill the gap, we develop a novel visual query system of massive taxi trajectory data directly supporting various visual analytics tasks over text based searches (i.e. name queries). Taxi trajectories are converted into “taxi documents” through a transformation process named textualization. This process can map a taxi’s path into a series of street/POI names and convert vehicle’s speeds into user-defined descriptive terms. Then a corpora of taxi documents is formed and indexed to provide flexible name queries and fast performance. Moreover, we develop a set of visualization tools for interactive visual analytics of query results. A set of unique tasks and case studies are presented to show the usability of the system..