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Conference Papers

* Co-first author    † Corresponding author

  1. Chen, J., Song, Y., Zhang, Y., Zeng, Z., Zhang, X., Pitafi, Z., Xie, Z., Das, D.K., Dong, N., Lu, J. and Yin, X., 2025. Selfdenoiser: self-supervised seismic signal denoiser for continuous and contactless cardiac monitoring. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 9(4), pp.1–31. (CORE A*, acceptance rate 24%) [UbiComp]
  2. Huang, Z., Zhang, E., Qiu, W., Cai, Y., Yang, C., Chen, E., Zhang, X., Ying, R., Zhou, D. and Yan, Y., 2026. Seeing through the brain: new insights from decoding visual stimuli with fMRI. International Conference on Learning Representations (ORAL). (CORE A*) [ICLR]
  3. Huang, N., Wang, H., He, Z., Zitnik, M. and Zhang, X.†, 2026. Repurposing foundation model for generalizable medical time series classification. International Conference on Learning Representations. (CORE A*) [ICLR]
  4. Wu, C., Wang, H., Zhang, X., Zhang, C. and Bu, J., 2025. Efficient personalized adaptation for physiological signal foundation model. Proceedings of the 42nd International Conference on Machine Learning, PMLR 267, pp.67833–67851. (CORE A*, acceptance rate 27%) [ICML]
  5. Wang, Y., Li, T., Yan, Y., Song, W. and Zhang, X.†, 2025. Why do medical time series models for disease detection generalise poorly to unseen subjects? Brain-Machine Interface Workshop, IEEE International Conference on Systems, Man, and Cybernetics.
  6. Huang, N., Tzallas, A., He, Z. and Zhang, X.†, 2025. Comparative analysis of foundation models for EEG-based Alzheimer's disease detection. International Joint Conference on Neural Networks. (CORE A) [IJCNN]
  7. Song, Y., Xiang, H., Zeng, Z., Zhang, X., Dou, F. and Song, W., 2025. Multi-granularity supervised contrastive learning with online adaptation for contactless in-bed posture classification. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 9(2). (CORE A*, acceptance rate 25%) [UbiComp]
  8. Li, T., Yan, Y., Song, W. and Zhang, X.†, 2025. Optimal EEG channel selection for Alzheimer's disease detection: an exhaustive analysis. Brain-Machine Interface Workshop, IEEE International Conference on Systems, Man, and Cybernetics.
  9. Wang, Y.*, Huang, N.*, Li, T.*, Yan, Y. and Zhang, X.†, 2024. Medformer: a multi-granularity patching transformer for medical time-series classification. Advances in Neural Information Processing Systems, 37. (CORE A*, acceptance rate 26%) [NeurIPS] [arXiv] [Code]
  10. Wu, C., Wang, H., Zhang, X., Fang, Z. and Bu, J., 2024. Spatio-temporal heterogeneous federated learning for time series classification with multi-view orthogonal training. Proceedings of the 32nd ACM International Conference on Multimedia. (CORE A*, acceptance rate 24%) [ACM MM]
  11. Song, Y., Pitafi, Z.F., Dou, F., Sun, J., Zhang, X., Phillips, B.G. and Song, W., 2024. Self-supervised representation learning and temporal-spectral feature fusion for bed occupancy detection. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. (CORE A*, acceptance rate 26%) [UbiComp]
  12. Wang, L., Wang, J., Yu, H., Huang, L., Zhang, X., Chen, Y. and Xie, X., 2024. Fixed: frustratingly easy domain generalization with mixup. Proceedings of the Conference on Parsimony and Learning, PMLR 234, pp.159–178. [CPAL] [arXiv]
  13. Wang, Y.*, Han, Y.*, Wang, H. and Zhang, X.†, 2023. Contrast everything: a hierarchical contrastive framework for medical time-series. Advances in Neural Information Processing Systems, 36. (CORE A*, acceptance rate 26%) [NeurIPS] [arXiv] [Code]
  14. Li, G., Duda, M., Zhang, X., Koutra, D. and Yan, Y., 2023. Interpretable sparsification of brain graphs: better practices and effective designs for graph neural networks. Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp.1223–1234. (CORE A*, acceptance rate 22.1%) [KDD] [arXiv] [Code]
  15. Liu, Z., Alavi, A., Li, M. and Zhang, X.†, 2023. Self-supervised learning for time series: contrastive or generative? AI4TS Workshop, International Joint Conference on Artificial Intelligence. [arXiv]
  16. Zhang, X., Zhao, Z., Tsiligkaridis, T. and Zitnik, M., 2022. Self-supervised contrastive pre-training for time series via time-frequency consistency. Advances in Neural Information Processing Systems, 35. (CORE A*, acceptance rate 25.6%) [NeurIPS] [arXiv] [Code]
  17. Wang, Y., Khalili, M.M. and Zhang, X., 2022. Towards fair representation learning in knowledge graph with stable adversarial debiasing. ICDM Workshop on Knowledge Graphs, IEEE International Conference on Data Mining. [ICDM]
  18. Zhang, X., Zeman, M., Tsiligkaridis, T. and Zitnik, M., 2022. Graph-guided network for irregularly sampled multivariate time series. International Conference on Learning Representations. (CORE A*, acceptance rate 32.9%) [ICLR] [arXiv] [Code]
  19. Wang, L., Zhang, X., Jiang, Y., Zhang, Y., Xu, C., Gao, R. and Zhang, D., 2021. Watching your phone's back: gesture recognition by sensing acoustical structure-borne propagation. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 5(2). (CORE A*, acceptance rate 22.4%) [UbiComp]
  20. Zhang, X. and Zitnik, M., 2020. GNNGuard: defending graph neural networks against adversarial attacks. Advances in Neural Information Processing Systems, 33, pp.9263–9275. (CORE A*, acceptance rate 20.1%) [NeurIPS] [Code]
  21. Zhang, X., Yao, L., Wang, X., Zhang, W., Zhang, S. and Liu, Y., 2019. Know your mind: adaptive cognitive activity recognition with reinforced attentive convolutional neural networks. Proceedings of the 19th IEEE International Conference on Data Mining, pp.896–905. (CORE A*, long paper, acceptance rate 9%) [ICDM]
  22. Zhang, X., Yao, L. and Yuan, F., 2019. Adversarial variational embedding for robust semi-supervised learning. Proceedings of the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp.139–147. (CORE A*, acceptance rate 14%) [KDD] [arXiv] [Code]
  23. Dong, M., Yao, L., Wang, X., Benatallah, B., Zhang, X. and Sheng, Q.Z., 2019. Dual-stream self-attentive random forest for false information detection. International Joint Conference on Neural Networks. (CORE A) [IJCNN]
  24. Zhang, X., Chen, X., Dong, M., Liu, H., Ge, C. and Yao, L., 2019. Multi-task generative adversarial learning on geometrical shape reconstruction from EEG brain signals. International Conference on Neural Information Processing. (CORE A) [arXiv]
  25. Zhang, X., Chen, X., Yao, L., Ge, C. and Dong, M., 2019. Deep neural network hyperparameter optimization with orthogonal array tuning. International Conference on Neural Information Processing. (CORE A) [arXiv]
  26. Chen, X., Huang, C., Zhang, X., Liu, W. and Yao, L., 2019. Distributed expert representation learning in question answering community. International Conference on Advanced Data Mining and Applications.
  27. Zhang, X., Yao, L., Kanhere, S.S., Liu, Y., Gu, T. and Chen, K., 2018. MindID: person identification from brain waves through attention-based recurrent neural network. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2(3), art.149. (CORE A*, acceptance rate 20.3%) [UbiComp] [arXiv] [Code]
  28. Zhang, X., Yao, L., Sheng, Q.Z., Kanhere, S.S., Gu, T. and Zhang, D., 2018. Converting your thoughts to texts: enabling brain typing via deep feature learning of EEG signals. IEEE International Conference on Pervasive Computing and Communications, pp.1–10. (CORE A*, acceptance rate 16.5%) [PerCom] [arXiv] [Code]
  29. Zhang, X., Yao, L., Huang, C., Wang, S., Tan, M., Long, G. and Wang, C., 2018. Multi-modality sensor data classification with selective attention. Proceedings of the 27th International Joint Conference on Artificial Intelligence, pp.3111–3117. (CORE A*, acceptance rate 20.1%) [IJCAI] [arXiv]
  30. Chen, W., Wang, S., Zhang, X., Yao, L., Yue, L., Qian, B. and Li, X., 2018. EEG-based motion intention recognition via multi-task RNNs. Proceedings of the 2018 SIAM International Conference on Data Mining, pp.279–287. (CORE A) [SDM]
  31. Zhang, D., Yao, L., Zhang, X., Wang, S., Chen, W. and Boots, R., 2018. EEG-based intention recognition from spatio-temporal representations via cascade and parallel convolutional recurrent neural networks. Proceedings of the 32nd AAAI Conference on Artificial Intelligence, pp.1703–1710. (CORE A*, acceptance rate 24.6%) [AAAI] [arXiv]
  32. Ning, X., Yao, L., Wang, X., Benatallah, B., Zhang, S. and Zhang, X., 2018. Data-augmented regression with generative convolutional network. International Conference on Web Information Systems Engineering. (CORE A)
  33. Zhang, X., Yao, L., Huang, C., Sheng, Q.Z. and Wang, X., 2017. Intent recognition in smart living through deep recurrent neural networks. International Conference on Neural Information Processing, pp.748–758. (CORE A) [ICONIP] [arXiv] [Code]
  34. Zhang, X., Yao, L., Zhang, D., Wang, X., Sheng, Q.Z. and Gu, T., 2017. Multi-person brain activity recognition via comprehensive EEG signal analysis. International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services. (CORE A) [MobiQuitous] [arXiv]

Journal Articles

  1. Liu, Z., Alavi, A., Li, M. and Zhang, X.†, 2026. A unified contrastive-generative framework for time series classification. IEEE Transactions on Artificial Intelligence. (SJR Q1) [TAI] [arXiv]
  2. Li, T., Yan, Y., Dou, F., Song, W. and Zhang, X.†, 2026. Cross-subject generalisation for EEG decoding: a survey of deep learning methods. Progress in Biomedical Engineering, 8(2), art.022013. (SJR Q1) [PBE] [arXiv]
  3. Wang, Y., Kang, Z., Chen, B., Zhang, Y. and Zhang, X.†, 2026. Benchmarking ERP analysis: manual features, deep learning, and foundation models. IEEE Transactions on Biomedical Engineering. (SJR Q1) [TBME] [arXiv] [Code]
  4. Fan, C. and Zhang, X.†, 2025. Stock price nowcasting and forecasting with deep learning. Journal of Intelligent Information Systems.[JIIS]
  5. Gao, Y.*, Zhang, X.*, Sun, Z., Chandak, P., Bu, J. and Wang, H., 2024. Precision adverse drug reactions prediction with heterogeneous graph neural network. Advanced Science. (SJR Q1) [Adv. Sci.]
  6. Arif, A., Wang, Y., Yin, R., Zhang, X.† and Helmy, A., 2024. EF-net: mental state recognition by analyzing multimodal EEG-fNIRS via CNN. Sensors, 24(6), art.1889. (SJR Q1) [Sensors]
  7. Liu, X., Li, J., Liu, Z. and Zhang, X.†, 2024. Semi-supervised contrastive learning for time series classification in healthcare. IEEE Transactions on Emerging Topics in Computational Intelligence. (SJR Q1) [TETCI]
  8. Meng, W., Inampudi, R., Zhang, X., Xu, J., Huang, Y., Xie, M., Bian, J. and Yin, R., 2024. An interpretable population graph network to identify rapid progression of Alzheimer's disease using UK Biobank. AMIA Annual Symposium. [AMIA]
  9. Chen, W., Yang, J., Sun, Z., Zhang, X., Tao, G., Ding, Y., Gu, J., Bu, J. and Wang, H., 2024. DeepASD: a deep adversarial-regularized graph learning method for ASD diagnosis with multimodal data. Translational Psychiatry, 14, art.388. (SJR Q1) [Transl. Psychiatry]
  10. Wen, J., Zhang, X., Rush, E., et al., 2023. Multimodal representation learning for predicting molecule–disease relations. Bioinformatics, 39(2), art.btad085. (SJR Q1) [Bioinformatics]
  11. Wu, L., Wang, H., Chen, Y., Zhang, X., et al., 2023. Beyond radiologist-level liver lesion detection on multi-phase contrast-enhanced CT images by deep learning. iScience. (SJR Q1) [iScience]
  12. Liu, Z., Alavi, A., Li, M. and Zhang, X.†, 2023. Self-supervised contrastive learning for medical time series: a systematic review. Sensors, 23(9), art.4221. (SJR Q1) [Sensors]
  13. Ye, B., Yin, C., Zhang, X. and Yin, R., 2023. MTLNFM: multi-task learning to predict patient clinical outcomes with neural factorization machine. AMIA Annual Symposium.
  14. Zhang, X., Sumathipala, M. and Zitnik, M., 2021. Population-scale patient safety data reveal inequalities in adverse events before and during COVID-19 pandemic. Nature Computational Science, 1(10), pp.666–677. (SJR Q1) [Nature Computational Science] [Code]
  15. Zhang, X., Yao, L., Wang, X., Monaghan, J., McAlpine, D. and Zhang, Y., 2021. A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers. Journal of Neural Engineering, 18(3), art.031002. (SJR Q1) [JNE] [arXiv]
  16. Zhang, X., Yao, L., Dong, M., Liu, Z., Zhang, Y. and Li, Y., 2020. Adversarial representation learning for robust patient-independent epileptic seizure detection. IEEE Journal of Biomedical and Health Informatics, 24(10). (SJR Q1) [JBHI]
  17. Huang, C., Yao, L., Wang, X., Benatallah, B. and Zhang, X., 2020. Software expert discovery via knowledge domain embeddings in a collaborative network. Pattern Recognition Letters. (SJR Q2) [PRL]
  18. Xu, W., Zhang, X., Luo, C., Yao, L., et al., 2020. A multi-view CNN-based acoustic classification system for automatic animal species identification. Ad Hoc Networks. (SJR Q1) [Ad Hoc Netw.]
  19. Bai, L., Yao, L., Wang, X., Li, C. and Zhang, X., 2020. Deep spatial-temporal sequence modeling for multi-step passenger demand prediction. Future Generation Computer Systems. [FGCS]
  20. Zhang, S., Yao, L., Wu, B., Xu, X., Zhang, X. and Zhu, L., 2019. Unraveling metric vector spaces with factorization for recommendation. IEEE Transactions on Industrial Informatics. (SJR Q1) [TII]
  21. Zhang, X., Yao, L., Zhang, S., Kanhere, S.S., Sheng, Q.Z. and Liu, Y., 2019. Internet of Things meets brain-computer interface: a unified deep learning framework for enabling human-thing cognitive interactivity. IEEE Internet of Things Journal, 6(2), pp.2084–2092. (SJR Q1) [IoT-J] [arXiv]
  22. Zhang, X., Yao, L., Huang, C., Gu, T., Yang, Z. and Liu, Y., 2020. DeepKey: a multimodal biometric authentication system via deep decoding gaits and brainwaves. ACM Transactions on Intelligent Systems and Technology, 11(4), art.49. (SJR Q1) [TIST] [arXiv]

Books & Book Chapters

  1. Zhang, X., 2023. Machine learning for EEG-based neurological disorder analysis: Alzheimer's disease, epilepsy, and Parkinson's disease. Chapter 9 in Brain-Computer Interface and Applications (Graduate Textbook).
  2. Zhang, X. and Li, X. (eds.), 2023. Deep Learning Architecture and Applications. MDPI Publisher. [Link]
  3. Zhang, X. and Yao, L., 2021. Deep Learning for EEG-Based Brain Computer Interface: Representations, Algorithms and Applications. World Scientific Publishing. (First on-market book on deep learning for EEG analysis; companion code: 300 GitHub stars, 70 forks) [Link] [Code]

Abstracts & Posters

  1. Wang, Y., Huang, N., Mammone, N., Cecchi, M. and Zhang, X.†, 2026. EEG-based Alzheimer's disease detection with foundation model. Alzheimer's Association International Conference.
  2. Zhang, X., Wang, Y., Chandak, P. and He, Z., 2023. Deep learning for EEG-based Alzheimer's disease diagnosis. Alzheimer's Association International Conference. [AAIC]
  3. Zhang, X., 2018. Context-aware human intent inference for improving human machine cooperation. PerCom PhD Forum. [PerCom] [arXiv]