Stochastic Geometry

Stochastic geometry provides a powerful framework for modeling the random spatial distribution of objects and their interactions, enabling the analysis of complex systems with inherent uncertainty. Current research focuses on applying stochastic geometry to diverse areas, including wireless network optimization (e.g., using stochastic models to improve federated learning and ultra-reliable low-latency communications), texture synthesis for computer vision, and object pose estimation. These applications leverage stochastic models to improve system performance, design efficient algorithms, and generate realistic synthetic data, ultimately impacting fields ranging from telecommunications to robotics and 3D reconstruction.