Cyrus Shahabi

TL;DR: The inherent technical challenges in realizing the potential of Big Data are explored, with a focus on machine learning and artificial intelligence.

TL;DR: In this paper, the authors propose to model the traffic flow as a diffusion process on a directed graph and introduce Diffusion Convolutional Recurrent Neural Network (DCRNN), a deep learning framework for traffic forecasting that incorporates both spatial and temporal dependency in the traffic flows.

TL;DR: This work proposes a novel framework to support private location-dependent queries, based on the theoretical work on Private Information Retrieval (PIR), which achieves stronger privacy for snapshots of user locations and is the first to provide provable privacy guarantees against correlation attacks.

TL;DR: This paper proposes a novel approach to efficiently and accurately evaluate KNN queries in spatial network databases using first order Voronoi diagram, which outperforms approaches that are based on on-line distance computation by up to one order of magnitude, and provides a factor of four improvement in the selectivity of the filter step as compared to the index-based approaches.

TL;DR: This paper introduces a taxonomy for spatial crowdsourcing, and focuses on one class of this taxonomy, in which workers send their locations to a centralized server and thereafter the server assigns to every worker his nearby tasks with the objective of maximizing the overall number of assigned tasks.