- Recruiting: I am looking for Ph.D. students to join my group at RIT in Spring 2027 or Fall 2027.
- Students interested in working with me can check the Work With Me for application forms and next steps.
Research Interest
I received my Ph.D. from Virginia Tech in 2020, advised by Prof. Chang-Tien Lu. I previously served as an Assistant Professor at Mississippi State University before joining RIT. 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 →
① "Analogy learning": Generalized patterns across systems. Shared operators, conserved quantities, and structural invariants that transfer across unrelated graph domains.
② "Interplay learning": Cross-system behaviors. Behaviors that propagate between networks, where cross-edges carry the dynamics.
③ "Network Digital Twin": Executable replicas of coupled infrastructure. Traffic, power, and communication testbeds for stress-testing estimation and control.
Selected Paper
Full publicationCross-Network Dynamics
Proceedings of the AAAI Conference on Artificial Intelligence, 2024 code CORE A*
Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2025 CORE A*
Uncertainty Quantification
2025 IEEE International Conference on Big Data (BigData), 2025 CORE B
Proceedings of the 2023 SIAM International Conference on Data Mining (SDM), 2023 CORE A
Tutorial series: Uncertainty Quantification for Dynamical Networks WSDM 26'
Spectral Graph Theory
ACM Computing Surveys, 2023
Tutorial series: Unifying Spectral and Spatial Graph Neural Network slides CVPR 24'SIAM MDS 24'CIKM 24'SIAM SDM 25' Awesome Spectral GNN
2018 IEEE International Conference on Data Mining (ICDM), 2018 CORE A*
Spatial Forecasting
Proceedings of the 2022 SIAM International Conference on Data Mining (SDM), 2022 CORE A
Proceedings of the 28th International Conference on Advances in Geographic Information Systems, 2020 CORE A
Proceedings of the 28th International Conference on Advances in Geographic Information Systems, 2020 Best Paper Award CORE A
Software / Hardware
Empirical Software Engineering, 2024
2020 Design, Automation & Test in Europe Conference & Exhibition (DATE), 2020 CORE A
Symbolic Discovery
Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems, 2025 CORE A
Science
2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2024
Nature Communications, 2019 Top 50 Chemistry & Materials Sciences article
Music
2017 IEEE International Conference on Data Mining (ICDM), 2017 code CORE A*
Selected Repo
Full repo- Spectral Graph Survey-driven entry point to spectral graph theory, graph signals, and modern graph learning. 175
- Fusion GAN Music generation project on genre fusion with adversarial dual learning. 19
- XFlow Python library for graph flow simulation, diffusion, and network dynamics experiments. 147
- SumoXPypsa Coupled transportation-power simulation stack behind the WWW 2026 line of work. 2