Research Interest

I study behaviors in dynamical and structural systems — graph-structured, dynamic, uncertain, and knowledge-rich — and how to estimate and control them. Recently I focus on cross-system behaviors and generalized patterns across very different real-world systems, including traffic grids, power networks, epidemics, social networks, banking, supply chains, and most recently large language models viewed as dynamical systems with structural representations. Techniques I've been working on include spectral / functional analysis and uncertainty quantification (UQ).

Relevant support: NSF CAREER #2443266 · NSF CIRC #2345921 · all grants →

Generalized patterns across systems

① "Analogy learning": Generalized patterns across systems. I look for shared operators, conserved quantities, and structural invariants that transfer across otherwise unrelated graph domains.

Cross-system behaviors

② "Interplay learning": Cross-system behaviors. I model and control behaviors that propagate between heterogeneous networks, where the most consequential dynamics live in the cross-edges.

Selected Repo

Full repo

Spectral Graph

Survey-driven entry point to spectral graph theory, graph signals, and modern graph learning.

Fusion GAN

Music generation project on genre fusion with adversarial dual learning.

XFlow

Python library for graph flow simulation, diffusion, and network dynamics experiments.

SumoXPypsa

Coupled transportation-power simulation stack behind the WWW 2026 line of work.

Selected Paper

Full publication
Network Interdiction Goes Neural
Lei Zhang, Zhiqian Chen, Chang-Tien Lu, and Liang Zhao
Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2025 Cross-Network CORE A*