Ben Shneiderman (www.cs.umd.edu/~ben) is a Professor in the Department of Computer Science and Founding Director (1983–2000) of the Human-Computer Interaction Laboratory (www.cs.umd.edu/hcil) at the University of Maryland. He is a Fellow of the ACM, IEEE, and AAAS, and a Member of the U.S. National Academy of Engineering. Ben Shneiderman is the co-author with Catherine Plaisant of Designing the User Interface: Strategies for Effective Human-Computer Interaction (5th ed., 2010), www.awl.com/DTUI. With Stu Card and Jock Mackinlay, he co-authored Readings in Information Visualization: Using Vision to Think (1999). With Ben Bederson he co-authored The Craft of Information Visualization (2003). His book Leonardo’s Laptop appeared in October 2002 (MIT Press) and won the IEEE book award for Distinguished Literary Contribution. His latest book, with Derek Hansen and Marc Smith, is Analyzing Social Media Networks with NodeXL (www.codeplex.com/nodexl, 2010).

The remarkable adoption of information visualization has triggered worldwide application from researchers, companies, governments, and news media. The public interest in visually rich infographics has raised questions of how best to present insights and what interactive capabilities enable engaging exploration, while maintaining comprehensibility. Government professionals find that compelling visual presentations inform policy making. At the same time researchers and corporate decision makers have increased expectations of how visual analytics can support important discoveries and business decisions. Our research community is racing to deal with complex data, rich questions, and large-scale data streams. This talk will present some success stories and suggest promising research directions.

Chandrajit Bajaj is the director of the Center for Computational Visualization, in the Institute for Computational and Engineering Sciences (ICES) and a Professor of Computer Sciences at the University of Texas at Austin. Bajaj holds the Computational Applied Mathematics Chair in Visualization. He is also an affiliate faculty member of Mathematics, Electrical and Bio-medical Engineering, Neurosciences, and a fellow of the Institute of Cell and Molecular Biology. He is on the editorial boards for the International Journal of Computational Geometry and Applications, the ACM Computing Surveys, and the SIAM Journal on Imaging Sciences. He is a fellow of the American Association for the Advancement of Science (AAAS), and a fellow of the Association of Computing Machinery (ACM).

Discoveries in computational molecular – cell biology and bioinformatics promise to provide new therapeutic interventions to disease. With the rapid growth of sequence and structural information for thousands of proteins and hundreds of cell types, computational processing are a restricting factor in obtaining quantitative understanding of molecular-cellular function. Processing and analysis is necessary both for input data (often from imaging) and simulation results. To make biological conclusions, this data must be input to and combined with results from computational analysis and simulations. Furthermore, as parallelism is increasingly prevalent, utilizing the available processing power is essential to development of scalable solutions needed for realistic scientific inquiry. However, complex image processing and even simulations performed on large clusters, multi-core CPU, GPU-type parallelization means that naïve cache unaware algorithms may not efficiently utilize available hardware. Future gains thus require improvements to a core suite of algorithms underpinning the data processing, simulation, optimization and visualization needed for scientific discovery. In this talk, I shall highlight current progress on these algorithms as well as provide several challenges for the visualization community.