Geometric Computing on Uncertain Data

CG Week 2022

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 explored a variety of data uncertainty models and problem-specific approaches, demonstrating a breadth of interest and scope. At SOCG 2016, the first installment of this workshop was organized by Pankaj Agarwal, Nirman Kumar, Ben Raichel and Subhash Suri. Since then the area has continued to expand, warranting another installment of the workshop to provide updates on previous topics, as well as to touch on new topics in uncertainty. 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. We also plan to more broadly survey the growth in the area of uncertainty and take stock of where the area is heading.


Friday, June 10

Time Speaker Title/Slides
14:30--14:40 Maarten Löffler / Benjamin Raichel Introduction and Overview
14:45--15:25 David Kirkpatrick Query Strategies for Maintaining Low Potential Congestion/Interference Among Moving Entities
15:30--16:00 Coffee Break
16:00--16:40 Mohammad Farshi Imprecise Spanners
16:45--17:25 Kevin Buchin Uncertain Trajectories
17:30--18:00 Maarten Löffler / Benjamin Raichel Discussion

Talk Abstracts