AI-Powered Models for Transportation and Resilient Infrastructure Systems
Explore AI and machine learning applications in transportation and resilient infrastructure systems. Work on digital twins, cross-infrastructure interdependencies, and multimodal data fusion using cutting-edge computational frameworks.
AI-generated overview
Project Description
Project Overview
This PhD project focuses on developing AI-powered models and computational frameworks to address complex challenges in next-generation transportation systems and resilient, interconnected infrastructure. The research targets interdisciplinary problems at the nexus of transportation, AI/ML, and system resilience.
What You Will Do
Students will investigate AI and machine learning methods for transportation network modeling, study cross-infrastructure interdependencies, develop digital twins for cyber-physical-social systems, and explore collaborative sensing and multimodal data fusion. The project emphasizes real-world impact through innovative, data-driven approaches.
Expected Outcomes
Outcomes include novel AI/ML algorithms for improving transportation system resilience, enhanced understanding of interconnected infrastructure behavior, and computational platforms enabling digital twin simulations to support decision making across domains.
Why This Matters
This research will contribute to safer, more efficient, and resilient infrastructure systems that underpin society. By integrating AI insights with civil engineering challenges, it aims to transform how transportation and infrastructure adapt to emerging demands and interdependencies.
Entry Requirements
How to Apply
Eligibility
Supervisor Profile
Dr. Jiachao Liu is an incoming Assistant Professor at the University of Nebraska–Lincoln’s Department of Civil and Environmental Engineering. He specializes in AI-powered transportation system modeling and infrastructure resilience. Currently a Postdoctoral Research Associate at Carnegie Mellon University, he holds a Ph.D. in Civil and Environmental Engineering and an M.S. in Machine Learning from CMU, alongside degrees in Transportation Engineering. His research integrates AI, transportation, and complex systems modeling.