Even as scientific datasets have been growing at an exponential rate, the capabilities of the human visual system have remained unchanged. As a result, we have now reached a stage where the current generation datasets can easily overwhelm the limits of human comprehension. Visual scalability is rapidly emerging as one of the grand challenges in scientific data exploration. This has informed much of our recent research in developing saliency-guided techniques for large data visualization and analysis. Most of the time, most of the data is innocuous and unimportant and even considering it wastes precious time and resources. However, current visualization systems effectively assume a default, that every piece of data is equally important. In this talk I shall highlight some of the recent advances in building visualization systems for exploring visualization datasets that leverage the principles of perception of visual salience and machine learning to address the big data challenge.
Amitabh Varshney is the Director of the Institute for Advanced Computer Studies (UMIACS) and Professor of Computer Science at the University of Maryland at College Park. Varshney’s research focus is on exploring the applications of graphics and visualization in engineering, science, and medicine. He has worked on a number of research areas in graphics and visualization including visual saliency, summarization of large visual datasets, automatically generating multiresolution hierarchies, procedural textures, and rendering with points, images, meshes, and volumes. He is currently exploring general-purpose high-performance parallel computing using clusters of CPUs and Graphics Processing Units (GPUs). He has served in various roles in the IEEE Visualization and Graphics Technical Committee, including as its Chair 2008 – 2012. He received the IEEE Visualization Technical Achievement Award in 2004. He is a Fellow of IEEE.
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