There is a growing need for algorithms that utilize and operate gracefully under data uncertainty. The sources of data uncertainty can vary widely, from measurement noise to missing information and strategic randomness, among others.
Several computational geometry researchers have explored a variety of input models and problem-specific approaches, demonstrating a breadth of interest and scope. This research area, however, is still growing, and benefits from a broader exchange of ideas on, probabilistic vs. deterministic, discrete vs. continuous, and static vs. dynamic,  models of input uncertainty.

The workshop is planned to be held in person at CGweek'24 in Athens.