Journal:

1. A. Wijesinghe, S. Zhang, and Z. Ding, “Pfl-gan: when client heterogeneity meets generative models in personalized federated learning,” accepted to IEEE Transactions on Machine Learning in Communications and Networking, 2025.

2. A. Wijesinghe, S. Wanninayaka, W. Wang, Y. Chao, S. Zhang, and Z. Ding, “LaMI-GO: Latent Mixture Integration for Goal-Oriented Communications Achieving High Spectrum Efficiency”, accepted to IEEE Transactions on Neural Networks and Learning Systems, 2025. [Preprint]

3. W. Wang, B. Liu, S. Gao, J. Li, Y. Zhou, S. Zhang*, and Z. Ding “PhA-MOE: Enhancing Hyperspectral Retrievals for Phytoplankton Absorption Using Mixture-of-Experts”, in MDPI Remote Sensing, 2025 [Article Link].

4. Y. Ma, Y. Zhou, S. Zhang and Z. Ding, “Dual-GRE: Dual-Phase Enhancement in Radiomap Estimation Based on Graph Attention”, in IEEE Wireless Communications Letters, 2025 [Article Link].

5. A. Wijesinghe, S. Zhang, S. Wanninayaka, W. Wang and Z. Ding, "Diff-GO+: An Efficient Diffusion Goal-Oriented Communication System with Local Feedback", in IEEE Transactions on Wireless Communications, 2025 [Article Link].

6. Y. Zhou, A. Wijesinghe, Y. Ma, S. Zhang* and Z. Ding,"TiRE-GAN: Task-Incentivized Generative Learning for Radiomap Estimation", in IEEE Wireless Communications Letters, 2025 [Article Link]

7. Q. Deng, Y. Zhang, M. Li, S. Zhang* and Z. Ding, "Efficient Eigen-Decomposition for Low-Rank Symmetric Matrices in Graph Signal Processing: An Incremental Approach", in IEEE Transactions on Signal Processing, 2024. [Article Link; Code].

8. Q. Deng, S. Zhang, and Z. Ding, "Body Motion Segmentation via Multilayer Graph Processing for Wearable Sensor Signals",  in IEEE Open Journal of Signal Processing, 2024. [Article Link]

9. X.Li, S. Zhang, H. Li, et.al. "RadioGAT: A Joint Model-based and Data-driven Framework for Multi-band Radiomap Reconstruction via Graph Attention Networks", in IEEE Transactions on Wireless Communications, 2024. [Article Link]

10. S. Jing, S. Zhang, and Z. Ding, "Reinforcement Learning for Robust Header Compression under Model Uncertainty ", in IEEE Transactions on Machine Learning in Communications and Networking, vol. 2, pp. 1033-1044, 2024. [Article Link]

11. A. Wijesinghe, S. Zhang, and Z. Ding, "PS-FedGAN: an efficient federated learning framework with strong data privacy", in IEEE Internet of Things Journal, vol. 11, no. 16, pp. 27584-27596, Aug. 2024. [Article Link]

12. S. Zhang, B. Choi, F. Ouyang and Z. Ding, "Physics-Inspired Machine Learning for Radiomap Estimation: Integration of Radio Propagation Models and Artificial Intelligence," in IEEE Communications Magazine, vol. 62, no. 8, pp. 155-161, August 2024. [Article Link; Dataset]

13. S. Zhang, T. Yu, B. Choi, F. Ouyang, and Z. Ding, “Radio Map Inpainting of Missing Regions using Propagation Priority and Depth Map”, in IEEE Transactions on Wireless Communications, vol. 23, no. 8, pp. 9330-9344, Aug. 2024. [ Article Link]

14. S. Zhang, Q. Deng, and Z. Ding, "Signal processing over multilayer graphs: theoretical foundations and practical applications," in  IEEE Internet of Things Journal, vol. 11, no. 2, pp. 2453-2471, 15 Jan.15, 2024. [ Article Link]

15. S. Zhang, A. Wijesinghe, and Z. Ding, “RME-GAN: A Learning Framework for Radio Map Estimation based on Conditional Generative Adversarial Network,” in IEEE Internet of Things Journal, vol. 10, no. 20, pp. 18016-18027, 15 Oct.15, 2023. [Article Link; Code]

16. S. Zhang, Q. Deng, and, Z. Ding, "Multilayer graph spectral analysis for hyperspectral images," in EURASIP J. Adv. Signal Process, vol. 1, no. 92, pp. 1 - 25, Oct. 2022. [Article Link]

17. Q. Deng, S. Zhang, and Z. Ding, "An efficient hypergraph approach to robust point cloud resampling," in IEEE Transactions on Image Processing, vol. 31, pp. 1924 - 1937, Feb. 2022. [Article Link; Codes]

18. Q. Deng, S. Zhang, and Z. Ding, "Point cloud resampling via hypergraph signal processing," in IEEE Signal Processing Letters, vol. 28, pp. 2117 - 2121, Oct. 2021. [Article Link]

19.  S. Zhang, S. Cui, and Z. Ding, "Hypergraph spectral analysis and processing in 3d point clouds," in IEEE Transactions on Image Processing, vol. 30, pp. 1193 - 1206, Dec. 2021. [ Article Link]

20. S. Zhang, S. Cui, and Z. Ding, "Hypergraph spectral clustering for point cloud segmentation," IEEE Signal Processing Letters, vol. 27, pp.1655 - 1659, Sept. 2020. [Article Link]

21. S. Zhang, Z. Ding, and S. Cui, "Introducing hypergraph signal processing: theoretical foundation and practical applications", in IEEE Internet of Things Journal, vol. 7, pp. 639 - 660, Jan. 2020. [Article Link]

Conference:

1. M. Shandirasegaran, Y. Zhou, S. Zhang, and S. Zhang “Theoretical Analysis of the Selection Mechanism in Mamba:Training Dynamics and Generalization”, accepted to NeurIPS 2025 WCTD Workshop.

2. H. Liao, Y. Zhou, S. Zhang, and S. Zhang “On the Training Dynamics of Contrastive Learning with Imbalanced Feature Distributions: A Theoretical Study of Feature Learning”, accepted to NeurIPS 2025 UniReps Workshop.

3. Z. Yao, Y. Wang, S. Zhang, Y. Li, Z. Cai, and Z. Tian, “Efficient Multi-Worker Selection based Distributed Swarm Learning via Analog Aggregation”, accepted to 59th Asilomar Conference on Signals, Systems, and Computers [Preprint].

4. F. Guo, A. Wijesinghe, S. Zhang*, and Z. Ding, “Task-adaptive semantic communications with controllable diffusion-based data regeneration”, accepted to 2025 IEEE Global Communications Conference (GLOBECOM) Workshop. [Preprint]

5. Z. Yao, Y. Wang, S. Zhang, Y. Li, Z. Cai, and Z. Tian, “Multi-worker selection based distributed swarm learning for edge iot with non-i.i.d. data”, accepted to 2025 IEEE Global Communications Conference (GLOBECOM). [Preprint]

6. F. Guo, H. Zheng, X. Zhang, L. Chen, Y. Wang, and S. Zhang*, “DiSC-Med: Diffusion-based Semantic Communications for Robust Medical Image Transmission” accepted to 2025 IEEE Global Communications Conference (GLOBECOM). [Preprint]

7. A. Wijesinghe, W. Wang, S. Wanninayaka, S. Zhang, and Z. Ding, “TACO: rethinking semantic communications with task adaptation and context embedding”, accepted to 2025 IEEE Global Communications Conference (GLOBECOM). [Preprint]

8. Q. Song, S. Jing, S. Zhang, S. Zhang, and C. Huang, "Mixture-of-Experts for Distributed Edge Computing with Channel-Aware Gating Function", in 2025 IEEE International Conference on Communications Workshops (ICC Workshops), Montreal, QC, Canada, 2025, pp. 1353-1358. [Article Link]

9. D. Yang, Y. Wang, S. Zhang, Y. Li, and Z. Cai, “Physics-Inspired Distributed Radio Map Estimation”, in ICC 2025 - IEEE International Conference on Communications, Montreal, QC, Canada, 2025, pp. 1025-1030. [Article Link]

10. Y. Chao, Y. Chen, W. Wang, A. Wijesinghe, S. Wanninayaka, S. Zhang, and Z. Ding, “Task-Driven Semantic Quantization and Imitation Learning for Goal-Oriented Communications”, in ICC 2025 - IEEE International Conference on Communications, Montreal, QC, Canada, 2025, pp. 1-6 . [Articile Link]

11. Y. Zhou, A. Wijesinghe, Y. Wang, S. Zhang*, and Z. Cai, "Efficient Transmission of Radiomaps via Physics-Enhanced Semantic Communications ", in ICC 2025 - IEEE International Conference on Communications, Montreal, QC, Canada, 2025, pp. 1175-1180. [Article Link]

12. S. Wanninayaka, A. Wijesinghe, W. Wang, Y. Chao, S. Zhang, and Z. Ding, “Diff-GOn: Enhancing Diffusion Models for Goal-Oriented Communications”, in ICC 2025 - IEEE International Conference on Communications, Montreal, QC, Canada, 2025, pp. 4535-4540. [Article Link]

13. X. Chen, X. Zhou, S. Zhang and M. Sun, “DualGFL: Empowering Federated Learning With a Dual-Level Coalition-Auction Game in Cooperative-Competitive Dynamics”, in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 39, no. 15, pp. 15904-15912. 2025. [Ariticle Link]

14. S. Jing, A. Yu, S. Zhang, and S. Zhang*, "Fedsc: provable federated self-supervised learning with spectral contrastive objective over non-i.i.d. data", in Proceedings of 41st International Conference on Machine Learning (ICML), Vienna, Austria, Jul. 2024, pp. 22304-22325. [Article Link]

15. A. Wijesinghe, S. Zhang, S. Qi and Z. Ding, "UFed-GAN: Secure Federated Learning over Wireless Sensor Networks with Unlabeled Data," 2024 IEEE International Conference on Communications Workshops (ICC Workshops), Denver, CO, USA, 2024, pp. 1048-1053. [Article Link]

16. X. Liu, L. Xu, X. Wu, S. Zhang and L. Wang, "Split-FL: An Efficient Online Federated Learning Framework with Constrained Computation and Streaming Data," 2024 IEEE International Conference on Communications Workshops (ICC Workshops), Denver, CO, USA, 2024, pp. 661-666.

17. A. Wijesinghe, S. Zhang, S. Wanninayaka, W. Wang and Z. Ding, "Diff-GO: Diffusion Goal-Oriented Communications with Ultra-High Spectrum Efficiency," 2024 IEEE International Conference on Communications Workshops (ICC Workshops), Denver, CO, USA, 2024, pp. 1079-1084. [Article Link]

18. S. Zhang, T. Yu, J. Tivald, B. Choi, F. Ouyang, and Z. Ding, “Exemplar-based radio map reconstruction of missing areas using propagation priority”, GLOBECOM 2022 - 2022 IEEE Global Communications Conference, Rio de Janeiro, Brazil, 2022, pp. 1217-1222. [Article Link]

19. S. Zhang, S. Cui, and Z. Ding, "Hypergraph-based image processing", 2020 IEEE International Conference on Image Processing (ICIP), Abu Dhabi, UAE, Oct. 2020, pp. 216-220. [Best Paper Finalist; Article Link].

20. S. Zhang, S. Cui and Z. Ding, "Point cloud segmentation based on hypergraph spectral clustering," 2020 Information Theory and Applications Workshop (ITA), San Diego, CA, USA, 2020, pp. 1-1. [Article Link]

21. S. Zhang, H. Zhang, H. Li, and S. Cui, "Tensor-based spectral analysis of cascading failures over multilayer complex systems," 2018 56th Annual Allerton Conference on Communication, Control, and Computing (Allerton), Monticello, IL, USA, Oct. 2018, pp. 997-1004. [Article Link]

Book Chapter:

1. S. Zhang, and Z. Ding, "Challenges and Opportunities for Generative AI in Wireless Communications and Networking", in Generative AI for Communications Systems: Fundamentals, Applications, and Prospects, Wiley.