There is a growing need for geometric algorithms that can gracefully operate under data uncertainty. The sources of data uncertainty can vary widely, from measurement noise to missing information and strategic randomness, among others. A number of researchers within computational geometry have recently explored a variety of data uncertainty models and problem-specific approaches, demonstrating a breadth of interest and scope. The research, however, is still in a state of infancy, and ripe for a broader exchange of ideas. The goal of the workshop is to provide a forum for computational geometers interested in this topic to learn about the current state of the art, stimulate discussions about new directions and challenges, and to foster collaborations.
Wednesday, June 15
|2:30--3:00||Maarten Loffler||Where are we Going? Uncertainty in Motion|
|3:00--3:30||Jeff Phillips||Coresets for Uncertain Data|
|3:30--4:00||Yuan Li||On the Arrangement of Stochastic Lines in R^2|
|4:30--5:00||Nancy Amato||Dealing with Uncertainty in Sampling-Based Motion Planning|
|5:00--5:30||Guy Rosman||Using Coresets for Video Summaries|
|5:30--6:00||Don Sheehy||Sampling uncertain manifolds|