会议报告
ClaudioSilva New York University, USA
Dr. Silva serves as CUSP’s Head of Disciplines, Professor of Computer Science at NYU Poly, and a faculty member at Courant and the Center for Data Science. Previously, he held research and academic positions at the University of Utah, AT&T Labs-Research, IBM Research, Sandia National Laboratory and Lawrence Livermore National Laboratory. Dr. Silva has co-authored more than 200 technical papers and 11 U.S. patents, primarily in visualization, geometry processing, computer graphics, scientific data management, HPC, and related areas. His current research interests include Big Data and Urban Systems. He has served on more than 100 program committees, and he is currently on the editorial board of the ACM Transactions on Spatial Algorithms and Systems (TSAS), Computer Graphics Forum, Computing in Science and Engineering, Computer and Graphics, The Visual Computer, and Graphical Models. He received IBM Faculty Awards in 2005, 2006, and 2007, and several best paper awards. He is a Fellow of the IEEE.
We take the view that future advances in science, engineering, and medicine depend on the ability to comprehend the vast amounts of data being produced and acquired. Visualization is a key enabling technology in this endeavor: it helps people explore and explain data through software systems that provide a static or interactive visual representation. Despite the promise that visualization can serve as an effective enabler of advances in other disciplines, the application of visualization technology is non-trivial. The design of effective visualizations is a complex process that requires understanding of existing techniques and how they relate to human cognition. Visualizations need to be effective, efficient, and correct. This requires a combination of design and science to reveal information that is otherwise obscured. In this talk, we plan to discuss recent work on three main topics: 1) the need for research in visualization correctness; 2) an example of pairing ideas from machine learning and scientific visualization into a powerful analysis technique; and 3) recent efforts in urban data visualization.