Addressing Structural Distribution Shift in Explanations for Graph Neural Networks
Zhuomin Chen, Hojat Allah Salehi, Esteban Schafir, Xu Zheng, Jiaxing Zhang, Hua Wei, Jingchao Ni, Farhad Shirani, Dongsheng Luo.
I work on explainable and trustworthy machine learning, with a focus on graph learning, time series modeling, and multimodal reasoning. My research aims to develop faithful explanation methods and evaluation frameworks that make modern AI systems more transparent, reliable, and accountable in high-stakes settings.
My research lies at the intersection of explainable AI, graph machine learning, and time-series intelligence. More broadly, I am interested in building machine learning systems whose decisions can be interpreted, verified, and trusted.
- faithful explanation methods for graph neural networks
- robust evaluation frameworks for explainability
- time-series explanation and reasoning
- multimodal and human-centered machine learning
Preprints
- 2026 From Signals to Semantics: A Survey on Time Series Explainability through a Human-Cognitive Lens. Link
- 2026 GMAIS: Graph-based Memory for Agent Inference Scaling.
- 2026 Trajectory Graph Copilot: Pre-Action Error Diagnosis in LLM Agents. Link
- 2025 Robust Surrogate Modeling for Explanation-Induced Out-of-Distribution Shift in GNNs. Link
- 2025 Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework. Link
Publications
Explanation-Preserving Augmentation for Semi-Supervised Graph Representation Learning
Zhuomin Chen, Jingchao Ni, Hojat Allah Salehi, Xu Zheng, Esteban Schafir, Farhad Shirani, Dongsheng Luo.
Uncovering Insights of Compound Flooding with Data-Driven AI
Xu Zheng, Chaohao Lin, Sipeng Chen, Zhuomin Chen, Jimeng Shi, Jayantha Obeysekera, Jingchao Ni, Wei Cheng, Jason Liu, Dongsheng Luo.
F-Fidelity: A Robust Framework for Faithfulness Evaluation of Explainable AI
Xu Zheng, Farhad Shirani, Zhuomin Chen, Chaohao Lin, Wei Cheng, Wenbo Guo, Dongsheng Luo.
Generating In-Distribution Proxy Graphs for Explainable Graph Neural Networks
Zhuomin Chen, Jiaxing Zhang, Jingchao Ni, Xiaoting Li, Yuchen Bian, Md Mezbahul Islam, Ananda Mohan Mondal, Hua Wei, Dongsheng Luo.
RegExplainer: Generating Explanations for Graph Neural Networks in Regression Task
Jiaxing Zhang, Zhuomin Chen, Hao Mei, Dongsheng Luo, Hua Wei.
TimeX++: Learning Time-Series Explanations with Information Bottleneck
Zichuan Liu, Tianchun Wang, Jimeng Shi, Xu Zheng, Zhuomin Chen, Lei Song, Wenqian Dong, Jayantha Obeysekera, Farhad Shirani, Dongsheng Luo.
Professional Service
Reviewer for ICML, NeurIPS, ICLR, KDD, WWW, AAAI, ICDM, PAKDD, and journals including TPAMI, TKDE, TAI, TKDD, TMI, TBD, TNNLS, Expert Systems, and T-IFS.
Program Committee: ICML 2025, AAAI 2026, WWW 2026.
Teaching Experience
Teaching Assistant, Florida International University
- Course: Discrete Structures
- Course: Computer Data Analysis
Awards
- NSF Travel Award for the Doctoral Forum at SDM 2024
- Provincial Outstanding Graduate
- First / Second Class Scholarships at Qingdao University and Taiyuan University of Technology
Personal note
Outside of research, I enjoy going to concerts (I'm excited to see Bruno Mars and Ed Sheeran soon), watching the WNBA (currently rooting for the Dallas Wings because I'm a fan of Paige Bueckers, Azzi Fudd, and Li Yueru) and TV shows, and traveling (especially hiking in U.S. national parks). I am always happy to connect with people interested in machine learning, explainability, and trustworthy AI.