CAREER: Accelerating Spatial Network Design: An Uncertainty-Driven Predict-and-Optimize Learning Framework

Georgia Tech Research Corporation, Atlanta GA

Investigators

Abstract

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). Spatial networks are ubiquitous in nature and human society, examples include traffic networks, power grids, food supply networks, and molecular systems. The structures and configurations of spatial networks determine important properties of the respective spatial systems. Spatial network design, the problem of designing spatial network structures and configurations for desired outcomes, is thus in pressing need across many domains. This project will develop a data-driven framework that can achieve fast and resilient spatial network design. The uniqueness of the project is that it tightly integrates predictive models into optimization algorithms for fast spatial network design, while accounting for the inherent system uncertainty. The project will help address many pressing societal challenges, such as optimizing a traffic network to mitigate congestion, distributing vaccines over the human mobility network to contain disease spread, and synthesizing new molecules that lead to environment-friendly materials. Technically, this project will develop a "predict-and-optimize" learning framework to achieve fast and resilient spatial network design. It will address three key challenges to this end. First, it will develop uncertainty-aware deep predictive models for spatial networks by modeling complex spatiotemporal dependencies while capturing the inherent uncertainty of the system. Second, it will integrate uncertainty-aware predictive models into optimization and generation algorithms, to effectively search the vast design space. Third, it will address the data scarcity issue in spatial network design by leveraging uncertainty for interactive data collection and label-efficient learning. The developed tools will be open-sourced and disseminated for spatial network design problems in various domains. Finally, this project will train the next generation of students and workforce and also promote diversity in data science education. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.