Weiliang Chen

1012 Boston Ave

Texas Tech University

United States, TX 79409

I am currently a Ph.D. student in Computer Science at Texas Tech University, advised by Prof. Song Liao. My interest is in the field of AI Security and Data Privacy. My work focus on developing robust and trustworthy intelligent systems, with an emphasis on privacy-preserving techniques, adversarial robustness, and secure data sharing in AI-driven applications.

I received both my Bachelor’s and Master’s degrees from the School of Computer and Big Data at Heilongjiang University, where I was advised by Prof. Qianqian Ren. During my graduate studies, I conducted research in data mining, reinforcement learning and federated learning, which laid a strong foundation for my current doctoral work.

News

Jun 20, 2025	Received the Outstanding Master’s Thesis award from Heilongjiang University!

Selected Publications

TIFS

Adversarial Self-Supervised Learning for Secure and Robust Urban Region Profiling

Weiliang Chen, Qianqian Ren^†, Yong Liu, Jianguo Sun^†, and Feng Lin

IEEE Transactions on Information Forensics and Security, 2025

DOI Bib PDF

@article{chen2025enhancedurbanregionprofiling,
  author = {Chen, Weiliang and Ren, Qianqian and Liu, Yong and Sun, Jianguo and Lin, Feng},
  journal = {IEEE Transactions on Information Forensics and Security},
  title = {Adversarial Self-Supervised Learning for Secure and Robust Urban Region Profiling},
  year = {2025},
  volume = {20},
  number = {},
  pages = {8124-8138},
  keywords = {Contrastive learning;Robustness;Perturbation methods;Forecasting;Training;Generators;Soft sensors;Semantics;Security;Resilience;Urban Region Profiling;Adversarial Contrastive Learning;Robust Forecasting;Adversarial Attacks;Smart City Security},
  doi = {10.1109/TIFS.2025.3594165}
}

IOTJ

Reputation-Driven Asynchronous Federated Learning for Enhanced Trajectory Prediction With Blockchain

Weiliang Chen, Li Jia, Yang Zhou^†, and Qianqian Ren^†

IEEE Internet of Things Journal, 2025

DOI Bib PDF

@article{10750313,
  author = {Chen, Weiliang and Jia, Li and Zhou, Yang and Ren, Qianqian},
  journal = {IEEE Internet of Things Journal},
  title = {Reputation-Driven Asynchronous Federated Learning for Enhanced Trajectory Prediction With Blockchain},
  year = {2025},
  volume = {12},
  number = {6},
  pages = {7405-7420},
  keywords = {Trajectory;Vehicle dynamics;Data models;Blockchains;Computational modeling;Federated learning;Servers;Accuracy;Predictive models;Decision making;Asynchronous federated learning (AFL);data sharing;deep reinforcement learning (DRL);differential privacy (DP);graph convolutional network;trajectory prediction},
  doi = {10.1109/JIOT.2024.3495693},
}

CIKM
Region-Wise Attentive Multi-View Representation Learning For Urban Region Embedding

Weiliang Chen and Qianqian Ren^†

In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, Birmingham, United Kingdom, 2023

Abs DOI Bib PDF

Urban region embedding is an important and yet highly challenging issue due to the complexity and constantly changing nature of urban data. To address the challenges, we propose a Region-Wise Multi-View Representation Learning (ROMER) to capture multi-view dependencies and learn expressive representations of urban regions without the constraints of rigid neighbourhood region conditions. Our model focuses on learn urban region representation from multi-source urban data. First, we capture the multi-view correlations from mobility flow patterns, POI semantics and check-in dynamics. Then, we adopt global graph attention networks to learn similarity of any two vertices in graphs. To comprehensively consider and share features of multiple views, a two-stage fusion module is further proposed to learn weights with external attention to fuse multi-view embeddings. Extensive experiments for two downstream tasks on real-world datasets demonstrate that our model outperforms state-of-the-art methods by up to 17% improvement.
@inproceedings{10.1145/3583780.3615194, author = {Chen, Weiliang and Ren, Qianqian}, title = {Region-Wise Attentive Multi-View Representation Learning For Urban Region Embedding}, year = {2023}, isbn = {9798400701245}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3583780.3615194}, doi = {10.1145/3583780.3615194}, booktitle = {Proceedings of the 32nd ACM International Conference on Information and Knowledge Management}, pages = {3763–3767}, numpages = {5}, keywords = {graph attention, graph neural network, multi-feature fusion, urban region embedding}, location = {Birmingham, United Kingdom}, series = {CIKM '23} }