Research

I embed traffic-flow theory into machine learning to model and estimate traffic dynamics at the network scale. This work includes physics-informed formulations of macroscopic models such as the generalized bathtub model for large-scale urban networks, graph learning guided by the network macroscopic fundamental diagram for traffic state imputation, and adaptive domain decomposition for estimating traffic states from sparse sensor data, aiming at scalable inference that stays reliable where data are limited.

I study how transportation networks absorb and recover from disruption. My work quantifies how climate-driven and cyber disruptions propagate through networks, how reliability degrades under such events, and how operations can be designed for faster and more dependable recovery, with a focus on reliability- and recovery-oriented modeling and decision support at the city scale.

I model and mitigate cyber threats to connected and autonomous mobility. This spans route guidance attacks that manipulate driver routing and their behavioral and network-level effects, detection of V2X attacks such as fake emergency messages, and a broader review of cybersecurity needs for next-generation road transportation, with defenses judged by their network-level outcomes.

I combine optimization, game theory, and behavioral modeling to study how travelers route and respond in mobility systems. This includes mean field routing games for connected and autonomous vehicles, analytical network-flow frameworks for traffic under route guidance attacks, and empirical studies of driver behavior such as gap acceptance and surrogate safety measures, connecting individual behavior to network-level performance.